With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only identifies potential anomalies but can also serve as a first step toward building predictive maintenance policies. In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. This work evaluates a combination of pre-processing techniques and machine learning (ML) models with a low computational cost. We use a combination of pre-processing techniques such as Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are well-known approaches for extracting features from raw data. We also aim to guarantee an optimal balance between multiple conflicting parameters, such as anomaly detection rate, false positive rate, and inference speed of the solution. To this end, multiobjective optimization and analysis are performed on the evaluated models. Pareto-optimal solutions are presented to select which models have the best results regarding classification metrics and computational effort. Differently from most works in this field that use publicly available datasets to validate their models, we propose an end-to-end solution combining low-cost and readily available IoT sensors. The approach is validated by acquiring a custom dataset from induction motors. Also, we fuse vibration, temperature, and noise data from these sensors as the input to the proposed ML model. Therefore, we aim to propose a methodology general enough to be applied in different industrial contexts in the future.

For design engineers, an artificial intelligence (AI) workflow encompasses four steps: data preparation, modeling, simulation and testing, and deployment. While all steps are important, many engineers often overemphasize the modeling stage, presuming that it plays the largest role in producing accurate insights. However, since data flows throughout the entire AI workflow, the initial data preparation step is crucial. It ensures that the most useful data is entered into a model. Figure 1 Data is the driving force in the development of an AI workflow.

Think about all the machines you use during a year, all of them, from a toaster every morning to an airplane every summer holiday. Now imagine that, from now on, one of them would fail every day. What impact would that have? The truth is that we are surrounded by machines that make our life easier, but we also get more and more dependent on them. Therefore, the quality of a machine is not only based on how useful and efficient it is, but also on how reliable it is.

In a previous post, we introduced an example of an IoT predictive maintenance problem. We framed the problem as one of estimating the remaining useful life (RUL) of in-service equipment, given some past operational history and historical run-to-failure data. Reading that post first will give you the best foundation for this one, as we are using the same data. In this post, we'll start to develop an intuition for how to approach the RUL estimation problem. As with everything in data science, there are a number of dimensions to consider, such as the form of model to employ and how to evaluate different approaches.