High equipment reliability is critical to the productivity and profitability of manufacturing companies. Traditional run-to-failure maintenance management is known to be vastly suboptimal because unscheduled downtime translates into high labor and maintenance costs and opportunity cost due to the idle time.
While preventative maintenance circumvents disruptions to operations, it incurs unnecessary cost by discarding many parts still in good working condition. An ideal maintenance policy takes into account the current condition of the mechanical components and schedules repair or replacement based on forecasted REMAINING USEFUL LIFE (RUL). This is predictive maintenance.
It is possible to get a read on the remaining useful life of a machine by monitoring the right predictor signal. For example, excessive vibration is a good indication that bearings are wearing out and component failure is an eventuality. While vibration signals are noisy in nature and defy simple cutoff rules, it is possible to characterize the gradual degradation process by means of artificial neural network training, as shown below.
In this illustration, the simple feed-forward neural network is trained using a sequence of vibration signals and its time indices and is able to reliably calculate the remaining useful life for test cases with widely different phase II durations.
A major part of the predictive maintenance challenge is in sorting out the relevant predictors in a sea of noisy and interdependent sensor signals. Before getting into the training phase, a complex event processing (CEP) effort must first be conducted to both sort out the candidate signals and identify the applicable regime of signal lifecycle. Often, subject matter experts provide important clues as a starting point. In such cases, a direct investigation of the event sequence, time window, and predicate relationships can be derived by an iterative computation.
In other cases, the complexity justifies the employment of various neural network techniques. Recurrent NEURAL NETWORKS (RNN), for example, are an excellent tool for the study of time-series events that occur in sensor data. Automatic mapping of raw signals into a high-dimensional space facilitates the classification of all the sensor inputs into clusters of similar behaviors. Advanced visualization allows investigators to efficiently identify useful patterns that can drive out the likely suspect signals.
Once signals are identified and deep learning models are trained, the entire predictive maintenance module can be deployed for real-time monitoring of warning signs and give operational staff clear metrics for scheduling optimal maintenance procedures. Typically, the runtime environment consists of a stream processing front end capable of picking out the early warning signs and the neural network executable, which makes prediction on these signals in their operational regime.