As an alternative, machine learning techniques for predictive maintenance can be used to produce a model that takes sensor data input to generate predictions without recourse to explicit physical knowledge. Machine learning comes in a variety of implementations, one of which is the so-called neural network. To some extent, a neural network mimics the workings of the human brain. It consists of a network of nodes, the artificial neurons, in which each connection between neurons is provided with a specific weight that has either a positive (excitatory) or a negative (inhibitory) value. Sensor data is fed as input to the model, which then has to generate a prediction about the behavior of the machine (“normal” or “abnormal” in the simplest, binary version) as its output.
The weights in a neural network can be modified, which influences the output of the model given a certain input. The model is first “trained” using so-called labelled data, meaning a set of known input and output data: sensor readings and the associated behavior, respectively. The set of weights, defining the model, is optimized to reproduce the known output. Then, in the real application, the model uses real-life sensor data to generate new output data, i.e. predictions.
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Machine learning also comes with a disadvantage. The relation between the input and output of a neural network is not represented by an understandable model, but by the set of weights, which means that reasoning about it is not straightforward. The big advantage, however, is that accurate predictions can be generated provided the model has been optimized with a high-quality training data set.
Domain knowledge is still required, to identify the critical condition parameters that have to be sensed. But domain knowledge is no longer required to build a realistic model. Instead, data scientists come in to build the machine learning model – and this can be used for diverse applications, such as condition monitoring.
Condition monitoring for predictive maintenance can be achieved in various implementations, one of which is anomaly detection. This concerns relevant physical quantities identified by the maintenance team, such as temperature, pressure, flow, and frequency spectra of natural vibrations. Their sensor readings are used as input to the neural network, which generates an output that is a measure of the (ab)normal functioning of the machine. If this measure exceeds a certain threshold value, as determined during the training phase, then abnormal anomalous behavior is detected. Expert judgement may still be required to assess whether a detected anomaly is real or just an artefact, for example due to sensor malfunctioning.
Another implementation of condition monitoring in manufacturing processes is the prediction of future sensor data based on their history, i.e. involving the processing of time-series sensor data. This calls for the use of a recurrent neural network, which is characterized by connections between nodes having a temporal direction. A specific form of such a recurrent neural network architecture is a long short-term memory network, which is particularly suited for producing predictions based on time-series data. If the real sensor data differ from the predicted values, this may signal a potential malfunctioning.
We have seen that machine learning for predictive maintenance offers multiple options for condition monitoring to facilitate accurate predictions and detect failures before they happen. A successful predictive maintenance implementation requires the combination of IoT expertise, domain knowledge and data science.
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