Abstracts
9-11 June 2026
The Grand, York, UK
[1A1] Dynamic survival analysis for aerospace systems: a landmarking approach for engine maintenance and fleet management
G Bahrini¹, E Morgue¹, S Razakarivony¹, M Barbet-Massin¹, R Bsili², D Idrissou², A Gariah² and F Faupin³
¹Safran Tech, France
²Safran Aircraft Engines, France
³Safran Helicopter Engines, France
Survival analysis (SA) studies the methods to estimate the remaining useful life of equipment. The integration of SA with longitudinal monitoring offers promising capabilities for predictive maintenance in aerospace systems. This study investigates the performance of landmarking techniques to predict future events from time-dependent covariates. Landmarking enables dynamic survival predictions by repeatedly updating models as new data become available, thus providing a flexible alternative to joint modelling for complex industrial datasets. Two application cases are presented to demonstrate the methodology’s effectiveness and practical value. The first focuses on predicting fleet withdrawals of helicopter engines based on operational monitoring parameters. The second examines the prediction of maintenance events for aircraft engines using operational and health monitoring parameters. Both studies compare the predictive accuracy of several landmarking variants against baseline static models. Results show that landmarking approaches achieve higher discrimination and calibration. The findings highlight the suitability of landmarking for real-time asset management and decision support within condition-based maintenance frameworks.
[1A2] Forecasting the unflown: counterfactual anomaly detection for aircraft engines via latent state modelling
T Binet¹, H Azzag¹, M Lebbah² and J Lacaille³
¹Sorbonne Paris Nord University, France
²Université Paris-Saclay, France
³Safran Aircraft Engines, France
Health monitoring of complex dynamical systems, particularly aircraft engines, traditionally focuses on remaining useful life (RUL) estimation. However, for operational planning, it is critical to predict how a specific engine, in a given degradation state, will respond to a future flight mission before it occurs. In this paper, the authors extend their state vector long short-term memory (SV-LSTM) model, an encoder-decoder architecture designed to learn a latent degradation representation decoupled from operational conditions to predict future flight parameters such as pressure and temperature in different modules of the engine. They propose a methodology where the engine’s state vector zt, inferred from historical data, is frozen, and the future flight command ut + 1 is replaced by a set of synthetic command profiles. By projecting these counterfactual scenarios through the model’s decoder, density distributions of critical observation parameters (for example temperatures/pressures) are generated for flights that have not yet been flown. Using anomaly detection on these simulated responses, the authors discriminate between two risk categories: (1) degradation mode identification, where a specific state vector exhibits abnormal response variance across a wide range of commands; and (2) critical mission detection, where specific flight profiles trigger extreme responses across a population of engines. This approach provides a preventive decision-making tool, assessing the compatibility between an engine’s health status and the severity of a planned mission.
[1A3] Virtual standardisation: benchmarking repair efficacy through counterfactual engine response simulation
T Binet¹, H Azzag¹, M Lebbah² and J Lacaille³
¹Sorbonne Paris Nord University, France
²Université Paris-Saclay, France
³Safran Aircraft Engines, France
Assessing the actual benefit of an aircraft engine shop visit from operational data is difficult because post-maintenance flights are generally performed under different missions and ambient conditions than pre-maintenance flights. As a result, raw before/after comparisons of engine parameters do not isolate the intrinsic effect of the repair itself and may lead to ambiguous maintenance assessment. In this paper, the authors propose a counterfactual framework for repair evaluation based on state vector long short-term memory (SV-LSTM), a self-supervised latent-state model that learns engine condition representations from operational time series. For a given engine state, the learned latent representation is frozen and evaluated under a shared library of standardised take-off contexts, rather than only under the flights experienced by the engine. Averaging the resulting simulated responses yields a standardised ZT49 (a temperature-related performance indicator at engine station 49) trajectory that removes a significant part of context-induced variability. Building on this trajectory, the authors define a standardised ZT49 repair gain computed before and after each shop visit, providing a context-normalised estimate of thermal recovery. Applied across a fleet, this metric makes it possible to compare maintenance events on a common basis and to rank repair families according to their expected thermodynamic benefit. The proposed approach therefore turns post-shop-visit assessment into a more objective decision support tool for workshop feedback and data-driven maintenance, repair and overhaul (MRO) optimisation.
[1A4] A stochastic event-generation simulator for dynamic risk assessment and maintenance decision support in aircraft systems
A Morel¹, M Oumar¹, C Ali¹, F Yazid¹, S de Souza¹, A Fredson¹, K Keltoum¹, D Amadou¹ and J Lacaille²
¹Sorbonne Paris Nord University, France
²Safran Aircraft Engines, France
This paper presents a stochastic event-generation simulator developed as a foundational component of a project, which aims to dynamically update risk assessments using operational data. The simulator models the degradation, inspection and failure dynamics of a system equipped with a single component and a single failure mode, providing a controlled environment for studying risk evolution and maintenance decision-making.
Component ageing follows a Weibull lifetime distribution, inspections occur according to a stochastic calendar, and measurement uncertainty is introduced through a beta-distributed inspection signal. A threshold-based detector triggers preventive or corrective maintenance actions, generating a detailed chronological log of inspections, failures, replacements, false alarms, missed detections and associated costs. Monte Carlo experimentation enables the analysis of how inspection intervals, detector accuracy and ageing parameters jointly influence operational risk and maintenance efficiency.
This simulator is a first step and already provides a ground-truth environment for validating future risk estimation algorithms, since the true underlying failure rates are known. Second, once calibrated on real operational data, it can support fleet-level event analysis, including residual risk estimation required when new failure modes emerge. Finally, the detector model contributes to the dynamic update of occurrence ratings, improving the reliability for future component design.
The contribution of this paper is threefold: the authors introduce an analytically controlled stochastic simulator specifically designed for validating dynamic risk estimation algorithms; they propose a simple but realistic fatigue-inspection-detection pipeline capturing ageing, imperfect measurements and maintenance actions; and they provide a fully open, reproducible implementation tailored for education and early-stage research.
[2A1] Condition monitoring and failure analysis of a reduced crude pump installed in an oil refinery
E Pereira
Glasgow Caledonian University, UK
Scheduled maintenance inspections are essential for ensuring safe and efficient operation in the oil & gas industry, covering both rotating and static equipment. There are several approaches an organisation can take to maintaining rotating machinery, and often an organisation practises a combination of different philosophies simultaneously. Vibration measurements also provide information that helps determine why the problem occurred in the first place. Machines are supposed to last much longer than they usually do. If improvements are made to the way the machine is installed, operated, maintained or even designed, the machine may require less maintenance in the future and will become more reliable. This paper presents a failure analysis of a reduced crude pump trip. Root cause failure analysis (RCFA) was performed, and the root cause event was the looseness of the closure nut, which is used to lock the impeller axially onto the shaft end. This situation could only be explained by a reversal of the train’s rotation. Vibration monitoring trends were analysed and presented.
[2A2] Condition monitoring and failure analysis of the dry gas seal on a centrifugal gas compressor installed in a liquefied natural gas plant
E Pereira
Glasgow Caledonian University, UK
Modern methods for inspection and maintenance planning safely improve production, increasing availability by preventing trips due to sudden equipment failure and incidents. This is done by identifying the risk drivers and root causes, introducing mitigating actions, improving inspection and maintenance, ensuring implementation and incorporating activities on the workflow. Dry gas seal (DGS) contamination is predominantly caused by inadequate quality and pressure of the gas in the seal cavity. 80% of DGS failures are due to seal gas contamination. This paper investigates the causes of gas compressor trips on the dry gas seal drive-end primary vent high-pressure alarm. A root cause failure analysis approach was used to analyse historical failure data, which indicates that the dry gas seal was heavily contaminated and lightly corroded, especially on the inboard housing external and internal surfaces. Also, in the primary stage, the O-ring located behind the carbon ring (inboard static face) shows evidence of being dislodged. This is typical of a reverse pressure issue (primary vent at a higher pressure than the casing). Condition monitoring graphics showing the dry gas seal behaviour are critically analysed and presented.
[2A3] Condition monitoring of the gas inlet flow control valve installed in a liquefied natural gas plant, external leakage packing failure and vibration analysis
E Pereira
Glasgow Caledonian University, UK
The source of equipment failure can start on the designer’s drafting board and end with poor maintenance practices and operating conditions. The way the machine is manufactured, the way it is installed and the way it is overhauled all contribute to the ultimate life of the machine. As mentioned earlier, the way the equipment is designed affects reliability. The procurement process affects reliability. The way in which the equipment is transported to the site and stored on the shelves affects reliability. And the way it is operated certainly affects reliability. Defect elimination is a name given to the proactive philosophy of looking for every cause of equipment failure and proactively seeking to eliminate those root causes. This paper investigates several sources of external leakage, packing failure and high vibrations in the gas inlet flow control valve installed in a liquefied natural gas (LNG) plant. A root cause failure analysis (RCFA) approach was used to analyse historical failure data. The primary root cause for external leakage is the presence of corrosion in the valve’s stuffing box due to rainwater accumulation on the bottom side of the actuator, which penetrates down between the gland follower and the stuffing box until it reaches the v-ring packing.
[2A4] Condition monitoring vibration survey on screw instrument air compressor package installed in a liquefied natural gas plant
E Pereira
Glasgow Caledonian University, UK
In the oil & gas industry, vibration measurement analysis plays a crucial role by helping detect early equipment degradation on both rotating and static equipment. So, equipment shutdown can then be planned accordingly in ways that do not disrupt normal plant operation. Vibration monitoring is all about reducing maintenance costs, improving production uptime and, in some cases, improving product quality. This paper presents the vibration survey performed on the screw instrument air compressor package, including the electrical motor, gearbox, pipework and screw compressor high-pressure (HP) and low-pressure (LP) elements. Component malfunctions were identified and critically analysed, and a safer and more reliable solution was recommended. Watch curves (VDI 3842 standard limits for piping vibration) were used. The frequency response function (FRF) indicates resonance on pipes. Experimental modal analysis (EMA) indicates a potential stress on the pipe located near the second stage outlet. Condition monitoring vibration trends and graphics are critically analysed and presented.
[2B1] Fault intelligent diagnosis of rotating machinery based on sound signal
S Yang, F Tu and T Zhang
Sichuan University, China
Intelligent fault diagnosis plays a critical role in the health monitoring of mechanical equipment. In this study, the authors present a systematic framework for intelligent fault diagnosis of rotating machinery using acoustic signals. The framework consists of signal preprocessing, multi-fault diagnosis and cross-domain fault diagnosis. First, sound signals collected from operating equipment using a microphone array are converted into Mel spectrograms for feature representation. Subsequently, multiple fault types are diagnosed and classified using a latent space–controlled generative adversarial network (GAN) with local perceptual capability. Furthermore, a cross-domain fault diagnosis network is developed based on adversarial domain generalisation with group optimisation to enhance robustness under varying operating conditions. Finally, the authors propose the concept of fault diagnosis without fault samples, which will be investigated in future work.
[2B2] Topological digital twins for remote condition monitoring
S Soleimanian¹, S Theodossiades² and R McKay¹
¹PSW Integrity Ltd, UK
²Loughborough University, UK
Online condition monitoring often suffers from false alarms, battery dependency, blind spots and signal loss. To address these issues, this research proposes a network-aware, physics-informed approach for monitoring rotary machines using topological digital twins. Each machine is represented as a connected system of sensing points, where links describe how parts are physically related and how vibration or fault signals move through the system. By applying vector-based analysis to this structure, the method helps better understand how the machine is organised, group related components, reduce signal interference and more reliably distinguish real faults from effects caused by nearby machines.
Building on these spectral features, the method enables several key monitoring capabilities. Dynamic thresholding adapts alarm limits according to machine interactions, reducing false alarms and improving alarm handling processes. The same graph structure supports virtual sensing, where missing or inactive sensor signals are reconstructed using information shared through the network. Additionally, spectral clustering provides automated interpretation of whether an observed anomaly originates locally or is transmitted through mechanical or spatial coupling.
The technique is validated through an industrial case study involving interconnected rotary machines. Results demonstrate that the approach is helpful in alarm reliability improvement and more accurate fault source identification. The proposed topological framework offers a scalable and interpretable foundation for next-generation remote condition monitoring systems.
[2B4] Quality of service and quality of experience investigations for video transmission over mobile ad hoc networks
A Abdussalam and R Saatchi
Sheffield Hallam University, UK
Condition monitoring and remote inspection increasingly rely on video-assisted assessment in environments where fixed communication infrastructure is unavailable, damaged or impractical to deploy. Mobile ad hoc networks (MANETs) offer a flexible multi-hop communication framework for such scenarios, but real-time video delivery over MANETs remains highly sensitive to delay, jitter and packet loss. In addition, network-layer performance and perceptual video quality are often assessed separately, leaving cross-layer degradation insufficiently characterised.
This paper presents a controlled evaluation of real-time H.264/AVC video streaming over an Optimized Link State Routing (OLSR)-routed MANET using NS-3.46.1 with partial emulation. Two fixed gateway nodes provided end-to-end connectivity between the simulated MANET and external sender and receiver processes operating in Linux network namespaces. Real encoded video traffic was transmitted through a GStreamer RTP/H.264/UDP pipeline in order to preserve codec-level packet-size variability and temporal frame structure. Ten node-density levels, from N = 6 to N = 60 in steps of 6, were evaluated with ten independent random seeds per density, yielding 100 initiated runs. Quality of service (QoS) metrics, including end-to-end delay, jitter, packet loss ratio (PLR), and goodput, were derived from PCAP traces, while quality of experience (QoE) metrics, namely peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), were obtained through full-reference FFmpeg-based video comparison.
The results identify a pronounced transition between N = 42 and N = 48 nodes. Across this interval, mean end-to-end delay increases from approximately 29 ms to 191 ms and the 95th percentile delay from approximately 100 ms to 636 ms, while goodput and PLR remain comparatively stable. This indicates that throughput alone is insufficient to detect the onset of degradation affecting video usability. The study therefore establishes a reproducible dual-layer baseline for evaluating adaptive optimisation methods intended to preserve usable video quality in MANET-supported remote inspection and condition monitoring scenarios.
Keywords: condition monitoring, remote inspection, mobile ad hoc networks, real-time video streaming, QoS, QoE, OLSR, NS-3.
[3A2] Emittance measurements on ambient materials using a thermal imager
J DeMonte
T5 Data Centers, USA
Procedurally, measuring a surface emittance with a thermal imager has required the target surface to be at a temperature that is higher or lower than the reflected apparent temperature. Some procedures require a 10-30°C difference between the reflected apparent temperature and the measured surface as a minimum.
This paper will discuss the possibilities of changing the reflected apparent temperature source instead of traditionally changing the target surface temperature. The results of this experiment will be published for the first time at CM 2026. The experiment will utilise targets that have both spectral and diffuse surfaces along with heated reflection sources while the target remains at ambient temperature.
[3A3] Non-destructive testing infrared thermography of an LPG refractory installed in a liquefied natural gas plant
E Pereira
Glasgow Caledonian University, UK
For many years and in many plants, the philosophy has been to keep the plant running. If machines fail, they are repaired, or spares are used. Little thought was given to improving equipment reliability or predicting failure. The maintenance department was a huge cost sink, and that was considered just a part of running the business. More recently, the philosophy has changed. Nowadays, organisations have begun to recognise that it is worth the investment of time and money to change maintenance practices and to work to improve equipment reliability. Great cost savings have been realised by this approach, often termed condition monitoring. Thermography is a popular technology applied to rotating and non-moving equipment in a plant. It involves the study of temperature, such as increased wear, steam leakage and electrical arcing, resulting in a change in temperature. Excessive heat is an indicator of problems or potential problems with plant equipment, including moving and stationary parts. Infrared thermography is an ideal non-intrusive technology for detecting these problems. This paper presents an infrared thermography scanning survey conducted on a liquefied petroleum gas (LPG) refractory to determine its current condition and identify any possible hotspots.
[3B1] Investigating the influence of condition-based maintenance on employee safety and economic outcomes on COMAH sites
J Watkinson, M Khan and J Orson
Cranfield University, UK
Control of Major Accidents and Hazards (COMAH) sites operate under frequent regulatory oversight to ensure they are maintaining standards. As the majority of these sites are industrial, operating under a traditional profit and loss model, there is a balance to ensure risk mitigation with cost efficiency.
This paper reviews the use of condition-based maintenance (CBM), as part of a wider predictive maintenance strategy. It offers a data-driven approach to identifying where CBM can positively impacting both safety and cost and specifically reviews the influence on safety and cost within the COMAH environment. It provides case studies where a mixed method approach has been adopted, combining a global industry survey with three case studies from a chemical manufacturing facility within the United Kingdom.
The methodology for this paper was through specific aims and objectives, which were delivered via an industrial survey and associated case study. Results indicated that 78.6% of the respondents found a reduction in safety incidents following CBM implementation, while 93% reported a positive return on investment. The survey alongside the case study demonstrated a cost saving exceeding £280,000 over an eight-month period, demonstrating mitigation of safety risks, including gas leaks, structural integrity concerns and vibration induced fatigue.
The findings highlight that CBM can positively influence both safety and economic outcomes when implemented within a structured framework supported by a proactive culture and organisational competence. However, barriers still remain in training and understanding of overall costs of CBM.
Keywords: maintenance management, condition-based maintenance, reliability, maintainability, motion amplification.
[3B3] Reliability beyond maintenance
P Price
Reliability Consultant, UK
This presentation outlines a modern, integrated approach to asset reliability and maintenance, demonstrating how advances in condition monitoring, data science and artificial intelligence can transform organisational performance. It reframes maintenance not as a collection of discrete maintenance tasks but as an information-driven process that links asset condition directly to business risk, operational efficiency and strategic decision-making.
The core message is that any activity generating information about an asset, whether from sensors, inspections, operations or user observations, should be treated as condition monitoring (CM). This expanded definition creates a unified data framework from which all maintenance and risk decisions can be derived. Building on this foundation, the concept of health status (HS) is introduced as a universal, standardised indicator that consolidates multiple data sources into a single representation of asset condition and risk.
The presentation shows that a CM-centred approach streamlines traditional methodologies such as failure mode and effects analysis (FMEA)/failure mode, effects and criticality analysis (FMECA), criticality, CM activity selection, planning and scheduling and enables the use of standardised models across asset classes. Beyond maintenance, the health status model supports decision-making (tactical, logistical, strategic) at all corporate levels. It enables more accurate forecasting, more efficient resource allocation and clearer visibility of operational and business risk.
Future developments in artificial intelligence have the potential to further enhance this by revealing hidden dependencies across the whole organisation, while quantum computing is identified as a future enabler of more powerful predictive capabilities.
[4B1] Intelligent stress and condition indicators for adaptation of control actions
E Juuso
University of Oulu, Finland
Efficient integration of condition monitoring with operation and maintenance provides important benefits for prognostics and health management. Intelligent stress and condition indicators have been developed for control and condition monitoring by combining generalised moments and norms with data-driven non-linear scaling. The features and scaled indicators can also focus on selected frequency ranges linked to specific components in a complex structure. The same scaling methodologies can be used for normal process measurements and performance measures used for management since management-oriented indicators can be presented in the same scale as intelligent condition and stress indices. Uncertainty, fluctuations and confidence in results are estimated by a difference of norms of high positive and negative order, respectively. The scaling approach brings temporal analysis to all measurements and features: trend indices are calculated by comparing the averages in the long and short time windows, a weighted sum of the trend index and its derivative detects the trend episodes and severity of the trend is estimated by also including the variable level in the sum. Risk indices are obtained from stress contributions. The solution is highly compact: all indices are in the range [–2, 2] and represented in natural language. Intelligent stress and condition indicators introduce useful information for adaptation of control and maintenance actions. The generalised statistical process control (GSPC) is a feasible tool for the early detection of fluctuations in operating conditions and fault detection.
Keywords: intelligent indicators, condition monitoring, vibration analysis, trend analysis, generalised statistical process control.
[5B1] Large fan aircraft engine bearing monitoring: present and future
S Greenfield
Anokhi Consulting Engineers, UK
What is oil debris monitoring? It involves looking at the contamination in a lubricating fluid to determine if the contamination can be directly related to a defect or whether it is the normal background-level contamination for that machine. It involves looking for a trend using oil samples or magnetic plugs. It is allied to but not the same as oil quality monitoring. Magnetic plugs have been the mainstay of early failure detection and have been improved with new technology offering remote monitoring and trending.
[5B2] Why normal isn’t always ‘normal’ in lubrication analysis
A Cutler
Oil Analysis Laboratories Ltd, UK
Oil analysis programmes across UK industry routinely generate laboratory data that is technically accurate yet operationally inert. Despite seven consecutive decades of investment in spectrometric, physical and particle-counting instrumentation, the gap between receiving a laboratory report and executing a maintenance action remains the single largest source of preventable mechanical failure in condition-monitored fleets. This article examines why the data-to-decision pathway fails, proposes a three-layer interpretation framework that separates contamination, degradation and wear signals into distinct decision channels and demonstrates through two field case patterns how rate-of-change analysis and tiered action workflows can transform oil analysis from a compliance exercise into a genuine failure prevention system. A practical decision workflow suitable for immediate site implementation is presented, together with an implementation roadmap designed for UK industrial operations seeking to close the gap between laboratory capability and maintenance execution.
[5B3] Multi-sensor diagnostic approaches for detecting fuel and hydraulic fluid contamination in marine engine oils
S Banerjee, T Mack, G Horwich and K Meissner
Gastops Ltd, Canada
Engine lubricating oil contamination presents a significant risk to machinery health, leading to decreased lubricant performance and accelerated component wear or failure. The potential of spectroscopic and electrical-based sensing methods for identifying and quantifying such contamination is investigated specifically in the context of marine diesel engines. In this environment, frequently sending samples for laboratory analysis may not be operationally feasible or can be cost prohibitive. Several complementary spectroscopic and electrical methods used in lubricant condition monitoring (CM) were evaluated and assessed for suitability for online integration. These included methods based on Fourier transform infrared spectroscopy (FTIR), fluorescence sensing, viscosity measurement and electrical permittivity. Each method was employed to measure a test set consisting of mixtures of marine engine oil with (a) diesel fuel and (b) hydraulic fluid in proportions consistent with application-relevant contamination levels. The results confirm that the utility of any given method used alone or in combination depends on features associated with the contaminant of interest. This study provides an approach to how online detection and quantification of engine oil contaminants may be achieved. This work can serve as the basis to enable condition-based maintenance strategies that can also strengthen the operational reliability of naval platforms.
[6A3] Integrating condition monitoring into an AI-enabled digital twin framework for dynamic FMECA and RCM implementation
A Khezam
University of Manchester, UK
Condition monitoring systems generate vast amounts of operational data critical for understanding asset health within the energy sector. However, traditional reliability methodologies such as failure modes, effects and criticality analysis (FMECA) and reliability-centred maintenance (RCM) often remain disconnected from these real-time data streams. This separation limits the ability of asset managers to dynamically adjust maintenance strategies in response to actual physical degradation. Building upon a previously developed framework for artificial intelligence (AI)-enabled reliability modelling, this paper presents an expanded approach that directly integrates condition monitoring telemetry into a continuous digital twin environment. The methodology utilises generative AI and machine learning to map live condition data, including vibration spectra and supervisory control and data acquisition (SCADA) outputs, directly to established failure modes. By continuously updating failure probabilities and criticality rankings based on the observed asset condition, the framework enables a closed loop system. This integration allows maintenance logic to evolve alongside component degradation, ultimately optimising maintenance scheduling and improving overall system availability for complex energy infrastructure.
[6A4] Application of a data-driven dynamic process modelling framework to improve the condition monitoring of a liquefied petroleum gas refrigeration unit
S Rahbarimanesh¹ and A Rahbarimanesh²
¹University of British Columbia, Canada
²University of Manchester, UK
With the fast-growing digitalisation of maintenance procedures in gas processing units, there has been a recent demand for data-driven condition monitoring (CM) tools that could competently diagnose the unit’s defective behaviour at off-design conditions and subsequently execute necessary adjusting measures. This demand mainly stems from the incapability of traditional process simulator packages in predicting the actual dynamic performance of such units, which not only leads to corresponding CM strategies becoming inaccurate in response, but, on a larger scale, makes the unit and its dependent downstream processes operate inefficiently at high maintenance costs. Towards addressing the noted matter for a practical case, this investigation proposes a data-driven CM framework for a malfunctioning refrigeration unit in a reference liquefied petroleum gas (LPG) plant, aiming to cost-effectively predict and resolve the unit’s faulty behaviour in a real-time sense. The framework is developed by customising the unit dynamic process simulators – through the actual history of unit operation attained from built-in measurement tools – and then coupling the simulator’s output to the plant control system for predetermined optimisation tasks. Considering the high adaptivity level of the proposed framework, it can be conveniently used as a supplemental tool for enhancing CM procedures in relevant processing plants.
[6A5] Application of supervised learning and multi-criteria decision making for condition monitoring of industrial assets
A Rahbarimanesh¹, M Burrows² and S Rahbarimanesh³
¹University of Manchester, UK
²RS Group plc, UK
³University of British Columbia, Canada
Condition monitoring (CM) is a critical component of industrial asset maintenance and management, particularly in the manufacturing context. CM identifies significant changes in a piece of machinery’s performance that could be indicative of a developing fault and potentially lead to significant operational cost and even major disruption in manufacturing and production.
Implementation of CM in a typical industrial environment requires support by a system of interconnected software and hardware elements. Traditionally, these systems were developed merely for the specific task of asset health monitoring. However, the digitalisation wave of Industry 4.0 and wider application of artificial intelligence-based (smart) technologies has provided a great opportunity for further development of these systems, thereby making substantial contributions to the efficiency of manufacturing and production.
As a part of a UK Government (InnovateUK)-funded project, an intelligent condition monitoring system (called JANUS) was designed and developed in the research and development (R&D) division of Monition Limited (now RS Group plc) in order to contribute to operational efficiency not only by means of reducing asset downtime via more accurate and on-time prediction of asset health condition but more efficient use of technicians/labour resources. In order to meet these objectives, JANUS used supervised learning-based machine learning algorithms along with multi-criteria decision-making techniques to develop an ML-enabled decision support system for the analysis of asset condition monitoring data.
[6A6] Project nautilUS +: towards an autonomous robotic technology for smart floor thickness monitoring of hazardous liquid storage tanks
A Rahbarimanesh¹ and M Burrows²
¹University of Manchester, UK
²RS Group plc, UK
Project nautilUS was a £1 million InnovateUK-funded project which has managed to develop a small intrinsically safe and potentially ATEX-certified robotic system in order to carry out in-service inspection on above-ground storage tanks (ASTs). Storage tanks are subject to corrosion over time with potential environmental consequences and it is expected that application of this robotic technology contributes to prevention of corrosion more efficiently and with less risks considering the significant cost and health & safety risks associated with manual inspection of these tanks.
The current nautilUS robotic system is semi-autonomous. It has the capability of moving around a tank floor and make measurements of the floor thinning using an ultrasound probe attached to it. The measurements along with location data are then recorded for post-processing after the robot is retrieved. However, this system is still dependent upon human/technicians' involvement, particularly when it comes to navigating the robot while it is inside the tank and analysis of floor thinning data once the data are collected and stored.
The Project nautilUS + is going to focus on the ‘smart’ element. The initial evaluations show that multi-criteria decision-making (MCDM) and machine learning (ML) algorithms can be used together innovatively in order to contribute to the autonomy of the nautiUS robotic system with the potential of making it fully autonomous.
[6B3] AI-based diagnostics and prognostics for vibration monitoring of a plastic-metal gear transmission
A Zippo, F Pellicano and M Molaie
University of Modena and Reggio Emilia, Italy
This paper presents a compact and fully data-driven framework for vibration-based diagnostics and prognostics of a plastic-metal gear transmission. 14 measurement runs, each lasting one minute and acquired one hour apart under constant operating conditions of 3000 r/min and 3 Nm, were used to induce the natural failure of the plastic pinion. The proposed pipeline processes the measurement files chronologically and converts them into a single health trajectory. The methodology combines block-wise feature extraction, baseline normalisation, principal component analysis (PCA), one-class support vector machine (OCSVM), isolation forest (IF) and a PCA-based reconstruction-error indicator.
The vibration data files were processed and segmented into 1178 blocks. From each block, 25 statistically and spectrally meaningful features were extracted; a baseline representing healthy behaviour was built from the first dataset using 70 blocks (80%). Diagnostics was performed using unsupervised anomaly detection and dimensionality reduction.
As mentioned, the methodology combines block-wise feature extraction, baseline normalisation, PCA, OCSVM, IF, and a PCA-based reconstruction-error indicator. The outputs of these complementary detectors are fused into a unified anomaly score, which is then mapped into a smoothed health index (HI). Remaining useful life (RUL) is estimated from the HI evolution by using four prognostic models, namely exponential degradation fitting (EXP), Gaussian process regression (GPR), autoregressive (AR) forecasting and particle filtering (PF), together with confidence intervals where available. The resulting workflow is unsupervised, interpretable and suitable for industrial case studies because it couples statistical indicators, anomaly fusion and multi-model RUL estimation within a single reproducible framework.
[6B4] Chaos characterisation in spiral bevel gears
A Zippo, F Pellicano and M Molaie
University of Modena and Reggio Emilia, Italy
This work further explores the non-linear dynamic behaviour of spiral bevel gear systems through advanced dynamical systems analysis techniques. Beyond stiffness modelling, the study provides an in-depth characterisation of the system response using tools from non-linear dynamics and chaos theory. The evolution of system behaviour is investigated via bifurcation structures, revealing transitions between periodic, multi-periodic and chaotic regimes, including period-doubling cascades and crisis-induced changes in attractor topology. Detailed dynamic analyses, including Poincaré maps and phase portraits, are employed to identify complex attractors and instability mechanisms such as tooth separation and irregular oscillations. Non-linear time-series analysis is applied to quantify system complexity through the estimation of the largest Lyapunov exponent and fractal dimension, confirming the presence of deterministic chaos in specific operating conditions. Furthermore, recurrence plots and recurrence quantification analysis (RQA) are utilised to distinguish between periodic and chaotic responses, providing additional insight into the temporal structure and predictability of the system. These analyses reveal subtle transitions and hidden dynamical features that are not captured by conventional vibration metrics. The results offer a comprehensive understanding of the underlying non-linear phenomena governing gear dynamics and demonstrate the effectiveness of combining classical mechanical modelling with modern non-linear analysis tools for diagnosing complex vibratory behaviour.
[6B5] Experimental study on post-buckling non-linear dynamics of a bio-inspired beam
A Zippo, F Pellicano and M Molaie
University of Modena and Reggio Emilia, Italy
This study investigates the non-linear dynamics of a bioinspired beam, with particular emphasis on the effect of temperature on its stability and vibrational characteristics. Although the thermal buckling and vibration of beams have been extensively studied through analytical and numerical approaches, experimental investigations addressing the combined effects of thermal loading, geometric non-linearity and damping evolution across buckling remain limited. To address this gap, a series of experimental tests, including impact, random excitation and stepped-sine measurements, are carried out to characterise the system dynamics. The experimental vibration tests are performed inside a climate chamber, where the bioinspired beam is base-excited by means of a shaking table in order to evaluate large-amplitude vibrations over a temperature range. The results reveal a pronounced non-monotonic evolution of the fundamental frequency, which decreases significantly as the temperature approaches the critical buckling condition, followed by a marked recovery in the post-buckling regime. In correspondence with the onset of buckling, the dynamic frequency-response curves display an interesting non-linear behaviour. These findings provide new experimental insight into the thermal dependence of non-linear vibrations in slender beam structures and offer useful guidelines for the design, reliability and performance optimisation of thermally loaded components.
[7A1] Early detection of rolling element bearing defects in a ball mill motor using multi-domain vibration analysis and AI-assisted diagnostics
A Mangouri¹, M Akbari¹ and S Atazadeh²
¹Islamic Azad University, Iran
²TPT Company, Iran
Early detection of bearing faults in heavy industrial machinery, particularly in ball mill drive systems, remains one of the key challenges in condition monitoring, as fault signatures are often obscured by variable loading conditions and high levels of background noise. In this industrial case study, a rolling element (ball) defect in an MRC 6319 bearing installed on a 132 kW ball mill motor operating at a nominal speed of 837 r/min was successfully identified at the pre-failure stage. A multi-domain vibration analysis was performed using the V-SAM analyser, which enables single-shot raw acceleration capture with simultaneous tri-channel acquisition. From a common baseline signal, multiple diagnostic features were extracted, including velocity RMS, acceleration spectra, high-frequency demodulation (HD) and peak demodulation (PD). The full synchronisation between these analytical domains enabled robust multi-domain cross-validation of the diagnostic results. At both the drive end (DE) and non-drive end (NDE) of the motor, a dominant ball spin frequency (BSF) component accompanied by clear fundamental train frequency (FTF) modulation was observed, indicating a ball defect with cage involvement. The physical vibration analysis results showed excellent agreement with the output of the artificial intelligence (AI)-assisted diagnostic module of the V-SAM software, which independently confirmed the bearing fault. This convergence between physics-based multi-domain analysis and AI-driven diagnostics demonstrates that integrating mechanical expertise with intelligent algorithms provides a powerful and forward-looking approach for early fault detection and predictive maintenance of heavy rotating equipment.
[7A2] Direct geometric information embedded in vibration autocorrelation: a case study on low-speed gear tooth profile measurement
A Mangouri¹, M Akbari¹ and S Atazadeh²
¹Islamic Azad University, Iran
²TPT Company, Iran
In vibration-based condition monitoring of gearboxes, autocorrelation is commonly employed as a statistical tool to enhance periodic components and reveal repetitive patterns in measured signals. However, its physical and mechanical interpretation is often limited and typically confined to the identification of dominant frequencies or modulation effects. This paper reports an unusual and meaningful experimental observation in an industrial power transmission system, where the measured tooth profile of a gear exhibits a striking direct geometric similarity to the autocorrelation function of the vibration signal acquired from the same system. The vibration data were collected from an industrial cement kiln gearbox comprising a single-stage pinion-gear pair operating under real working conditions. Measurements were performed using a VIBRO-SAM (V-SAM) vibration analyser, and the signal processing procedure included amplitude demodulation, band-pass filtering and autocorrelation analysis conducted under a low-speed, quasi-static operating regime. The results indicate that, under low rotational speed and relatively steady load conditions, the vibration autocorrelation function can embed direct geometric information related to the gear tooth profile, rather than serving solely as a statistical indicator of periodicity. This observation suggests a new mechanical interpretation of autocorrelation as an indirect representation of contact characteristics and geometric variations of gear teeth. The findings open a potential new pathway for exploiting classical signal processing tools to extract deeper physical information from industrial power transmission systems, particularly for condition monitoring of low-speed gearboxes.
[7A3] Machine learning-based bearing fault diagnosis with validation on industrial machinery
N Hanari¹, A Rikhtehgar Ghiasi¹, A Mangour², M Akbari² and S Atazade³
¹University of Tabriz, Iran
²Islamic Azad University, Iran
³TPT Company, Iran
Bearing faults are a major source of failure in rotating machinery used in industrial applications. Machine learning (ML) techniques have shown strong potential for bearing condition monitoring, enabling early fault detection and reducing unplanned downtime in industrial systems.
This paper presents a data-driven bearing fault diagnosis approach using vibration signals acquired from an experimental test-rig specifically designed and constructed for this purpose, as well as real industrial machinery. A comprehensive dataset of approximately 7000 samples from bearings of different sizes was collected with the support of TPT Co, covering four bearing conditions: outer race fault, inner race fault, rolling element fault and normal operation.
The ML models were trained using test-rig data obtained under multiple operating conditions, including different bearing types, rotational speeds and load levels. The trained models were subsequently evaluated under unseen conditions, including additional variations in bearing types, load levels and rotational speeds, as well as vibration signals collected from several industrial motors operating in various industries, including a cement plant, and the results were validated.
Statistical features were extracted from raw vibration signals, envelope and autocorrelation signals and used to train supervised ML classifiers. Among the evaluated classifiers, the XGBoost model achieved classification accuracies exceeding 90% across the tested conditions. These results demonstrate that the proposed approach is robust and practically applicable, with strong potential for reliable bearing fault diagnosis in real industrial environments.
Keywords: bearing fault diagnosis, machine learning, condition monitoring, vibration analysis, industrial machinery, time-domain features.
[7B1] The importance of multiple data sources for condition monitoring: a wind industry case study
D Hickey
Natural Power, UK
There is some terminology confusion within the wind industry surrounding definitions of vibration analysis and condition monitoring (CM). A good CM programme will incorporate every available data source into a decision-making model for reliability studies. If available, this will include vibration analysis. In the wind industry, a good reliability programme considers analysis from a wind turbine’s on-board vibration monitoring system, supervisory control and data acquisition (SCADA) unit, oil particle count sensor, tribology information and any visual representations of damage (from endoscopy or other). These analyses are used to make informed decisions on the health and status of the operating asset and in turn maximise the efficiency of the operating service and maintenance teams.
This paper attempts to use the industry-known prevention-failure (P-F) curve to demonstrate the importance of using multiple data sources, as described above, for the purposes of reliability. In doing so, real-world case studies will be provided demonstrating the challenges of early damage detection faced in the wind industry. The central aim is to correctly define the methodologies available and provide the cost-benefit analysis for each, both individually and combined.
[8A1] Progressive rotor bar fault diagnosis in a 9 MW induction motor: a vibration-based case study
A Mangouri¹, M Akbari¹ and S Atazadeh²
¹Islamic Azad University, Iran
²TPT Company, Iran
This paper presents a case study on advanced fault diagnosis of a 9 MW, four-pole squirrel-cage induction motor rated at 1500 r/min and supplied by a 50 Hz power network, which exhibited abnormal vibration behaviour and an elevated risk of progressive failure. The objective is to identify the dominant fault mechanism and to discriminate between mechanical and electromagnetic excitation sources using multi-level vibration analysis, supported by complementary condition monitoring techniques.
Initial vibration analysis revealed a dominant 1X running speed component in the horizontal direction with an approximately 90° phase difference relative to the vertical response, a pattern typically associated with rotor unbalance or structural effects. However, the simultaneous presence of high-frequency spectral components spaced at 2× line frequency (100 Hz) suggested a possible electromagnetic origin. Further high-frequency and impact-based analyses identified bearing-related frequencies, including ball pass frequency outer race (BPFO) and ball spin frequency (BSF), indicating advanced rolling element bearing damage. Correlation with slip-related spectral features demonstrated that bearing degradation alone could not explain the observed vibration behaviour.
The diagnostic focus was therefore shifted toward frequency components around the rotor bar pass frequency (RBF) and their symmetrical sidebands spaced at 2 × LF. The persistent presence of these components, together with pronounced far sidebands revealed through high-definition (HD) and peak detection (PD) analyses, provided strong evidence of a progressive rotor bar fault driven by slip-modulated electromagnetic asymmetry.
Based on the convergence of vibration signatures, advanced demodulation results and consistency with established electromagnetic fault models, the motor condition was classified as conditionally operable in accordance with vibration severity criteria for high-power machines. Operational recommendations included limiting start-stop cycles, avoiding prolonged low-load operation and implementing motor current signature analysis (MCSA) for independent fault verification, followed by scheduled non-destructive testing and post-repair dynamic balancing in compliance with ISO 1940-1, grade G2.5.
[8B1] AeroTwinX™: a physics-aware, AI-enabled digital twin framework for aero engines
N Vyas
Indian Institute of Technology Kanpur, India
The behaviour of aero engines arises from strong interactions between fuel delivery, gas-path thermodynamics, shaft rotordynamics, lubrication hydraulics and structural vibration. Modern testbeds measure these subsystems through multi-rate signals such as throttle position, r/min, exhaust gas temperature (EGT), fuel flow and pressure, oil pressure and temperature, compressor inlet pressure, ambient conditions, torque-meter pressure and high-frequency casing vibration. Conventional condition monitoring typically relies on trending and alarm thresholds, offering limited capability for reconstructing internal physical states or quantifying health in real time.
This talk will describe AeroTwinX™, a physics-aware, artificial intelligence (AI)-enabled digital twin framework developed to address these limitations for aero engine applications. The platform unifies multi-rate sensing through a common time-aligned architecture, transforming vibration measurements into STFT-based spectrograms while synchronising gas-path, fuel, oil and environmental parameters. A dual-head neural architecture simultaneously performs fault classification and regression of latent physical states such as shaft speed, thermal load and torque, ensuring diagnostic outputs remain consistent with underlying engine physics.
AeroTwinX incorporates a mathematically defined health index (HI) that combines diagnostic confidence with deviation of predicted internal states from learned healthy envelopes. This provides a continuous and interpretable measure of engine integrity, sensitive to both abrupt faults and gradual degradation. Case studies demonstrate how AeroTwinX advances aero engine monitoring from reactive fault detection to state-aware digital twinning, enabling predictive and health-centric decision support for propulsion systems.
For further information contact: Events and Awards Department, The British Institute of Non-Destructive Testing,
Midsummer House, Riverside Way, Bedford Road, Northampton NN1 5NX, UK.
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