ABB's Hybrid Approach
14/12/2021
ABB has combined machine learning (ML) and fault analysis algorithms to provide automated predictive maintenance for all kinds of applications across Industry 4.0.The advances in digital technology, machine learning and cloud and edge computing mean a new approach for asset health maintenance is emerging. Process industries rely on a multitude of crucial equipment such as motors, pumps, fans, compressors and turbines running around the clock to ensure smooth production. Keeping these machines at peak health is critical as wear and tear is inevitable.
However, forecasting and predicting maintenance is not straightforward and varies between industries. To effectively schedule maintenance, it is necessary to predict how a detected abnormal condition is likely to develop in the future; only then can valuable insight into the probable future consequences be gained.
The hybrid approach being used by ABB combines ML models and failure modes and effect analysis (FMEA) to provide accurate information about actual asset health.
Successful predictive maintenance requires a three-pronged process:
- Condition monitoring that can provide early detection of faults;
- Identification of specific failure mode(s) related to the fault detection; and
- Quantification of the extent of fault development to support maintenance planning.
Practical industrial experiences indicate that such approaches are not always successful and often lead to several types of false positive and false negative. Raising an alarm when the asset is completely healthy or vice versa increases unplanned costs.
