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. While machine learning has certainly brought reliability and predictive maintenance forward and is extensively leveraged these days by organizations that leverage best practices in predictive maintenance, there is no denying that even this approach has limitations. The other category of algorithms, deep learning algorithms, can address these limitations.

Limitations of ML approach for data-driven predictive maintenance

The very first limitation is generalizability. Because the implementation mechanism off from machine learning algorithms is specific to domains it translates into training and fine tuning of the algorithm for every specific application.

Domain-specific knowledge is a critical input in designing machine learning-based predictive maintenance algorithms. Also, preprocessing steps like feature engineering are almost always mandatory in machine learning-based fault detection prognostic or diagnostic algorithms. Remember that feature engineering is an intricate process requiring a careful combination of inputs gathered from domain experts and customized features that can structure the data set.

While certainly an advance over the legacy methods of maintenance, the fact is that because the network architecture of machine learning algorithms is simple, these networks still have limited learning capabilities. In technical jargon parlance, the term for such networks is shallow networks. However, the data leveraged for building data-driven predictive maintenance algorithms and processes has all the traits of real-world data, like noise, nonlinearity, and other complexities. 

Machine learning algorithms do not handle irregularities, nonstationarity, and nonlinearity, and these traits are generally present in sensor data collected from industrial equipment. This is why these shallow networks cannot perform data abstraction through features critical for fault predictions in the most efficient way. 

If you have implemented these algorithms in the real world for predictive maintenance after substantial training, you know that the actual real-world performance of these machine learning algorithms declines when they are used with real-time datasets in production. Machine learning algorithms also do not perform well in cross-domain applications. If the nature of the applications is complex, performance is impacted.

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.