The world of maintenance has made substantial progress in the last decade. Fueled by the ease of availability of machine learning tools like open source tools, organizations moving to the cloud, where most hyperscalers provide them a very robust tool chest to make the best use of their data through AI and machine learning, a significant number of manufacturing organizations have developed the capability of predictive maintenance, powered by machine learning.

Two broad categories of algorithms can be leveraged for predictive maintenance. One category is classic machine learning algorithms like clustering, SVM, and decision trees. However, as discussed in the subsequent section, these algorithms have certain disadvantages. This two-part article will examine the disadvantages and how deep learning algorithms can help mitigate those disadvantages. This first part covered the limitations of the machine learning algorithm group. 

This part will cover why deep learning algorithms may be a better choice for data-driven predictive maintenance.

Advantages of deep learning algorithms for data-driven predictive maintenance

Deep learning algorithms have the capability of performing nonlinear transformations in hierarchical order. This capability makes it easier to extrapolate noisy and coarse data without meeting prerequisites like feature extraction or feature selection. Overall, this allows automated learning of structures from fresh data. As there is no requirement for feature selection and extraction, developing a strategy for condition monitoring, fault detection, diagnosis, and prognosis strategy becomes much easier and gets accelerated.

While machine learning algorithms can also handle “Big Data”, deep learning performs better with massive amounts of data. In fact, it provides the best results when the data volume is huge. When we envision a plethora of sensors streaming constant data streams, deep learning algorithms seem like a great natural fit vs. machine learning algorithms.

Another advantageous area is the transfer learning capability. Transfer learning capabilities provide the capability to develop cross-domain, data-driven solutions. Since deep learning algorithms are more suitable for transfer learning, they are also a better choice from this perspective. An added advantage of transfer learning is that you can avoid repeated training processes. This obviously helps save computing power and time.

When it comes to generalization, deep learning-based algorithms can generalize much better than machine learning-based predictive maintenance algorithms. Multitasking is another area that is possible in deep learning algorithms. Multitasking learning capability allows the creation of multiple threads for various tasks instead of running a separate model for each task.

When it comes to very complex data and complex problems, the architecture of neural networks (large number of layers and neurons) provides a significant advantage over machine learning algorithms. The capability of NNs to automatically extract the correct features from the data adds to the attractive aspect.

If you had the notion, due to any form of gaslighting, that this is futuristic, deep learning algorithms are already being leveraged for predictive maintenance. You can find plenty of examples and some solutions that leverage deep learning. I will publish the results of my playing around, trying to build an LSTM-based predictive maintenance algorithm. The article will be published on 02/14.