预测algorithms allow you to avoid equipment failure by monitoring sensor data from machines to predict when a failure event will happen. Based on those predictions, you can adjust maintenance schedules. These prognostics algorithms provide an alternative to conventional preventive maintenance programs in which the maintenance schedule is determined by a prescribed timeline.
预测algorithms enable customers and equipment manufacturers to:
- Reduce equipment downtime by identifying issues before failure, thereby extending equipment lifetime
- Avoid the costs of unnecessary maintenance by scheduling equipment service only when necessary
- Bring equipment back online faster by determining the root cause of impending failures and faults
预测algorithms are critical to the success of predictive maintenance programs. Temperature, pressure, voltage, noise, or vibration measurements are collected using sensors. This data is processed using various statistical andsignal processingtechniques to extract features calledcondition indicators. To monitor the health of the equipment, you can compare these condition indicators with established markers of faulty conditions using data clustering and classification or othermachine learningtechniques. You can also use the condition indicators as inputs toremaining useful life (RUL) estimation modelsto train prognostics algorithms. The RUL model used—similarity-based, trend-based, or survival-based—depends on the kind of data available. The end result is a prognostics algorithm that can classify and predict the next failure event and provide a confidence bound on that prediction.
预测algorithm development workflow.
Once validated, prognostics algorithms can be operationalized in an IT environment such as a server or cloud. Alternatively, prognostics algorithms can be implemented in an embedded system directly on the equipment, enabling faster response times and significantly reducing the amount of data sent over the network.
For additional information, seePredictive Maintenance Toolbox™,Statistics and Machine Learning Toolbox™, andDeep Learning Toolbox™.
