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.
Three modes of maintenance:
- Corrective – donâ€™t fix until it breaks
- Preventive – schedule maintenance based on age only
- Predictive – schedule maintenance based on age and condition
- Cost matrix – optimize overall maintenance cost
Remaining Useful Life
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.
Application of NEURAL NETWORKS to RUL prediction
IoT data in real life can be much more complex and multifaceted than the vibration signals in the last example. The data can arrive with or without schema, in the textual, audio, video, imagery and binary form, sometimes multi-lingual and encrypted. They are often streamed in real time and may require substantial infrastructure upgrade in terms of storage, integration and analytic requirements. Once captured, relating the relevant predictor signals to the failure event is generally a challenging process befuddled by high dimensionality, obscure causality, and complex distributions.
NEURAL NETWORKS have properties that make them particularly useful for solving problems of this level of difficulty. Deep learning NEURAL NETWORKS are known for their strengths in the following areas:
- Implements regression or classification by applying nonlinear transformation to linear combinations of raw input features
- Hidden layers and high neuron counts increases the expressiveness of the Neural Network
- Self-learning through back propagation mechanism
- Auto detects composite events that serves as precursors to phenomenon of interest
- Excellent in multi-modal learning, matching multi-modality of IoT
- Well suited for temporal pattern recognition
CEP and stream processing
A major part of the predictive maintenance challenge is in sorting out the relevant predictors out of a sea of noisy and inter-dependent sensor signals sounding any one component. 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 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 occurs in sensor data. Automatic mapping of raw signals into a high dimensional space facilitates the classification 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 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.