M4E #103: Predictive Maintenance Explained
Everyone had some kind of event in their factories that caused ugly breakdowns due to unexpected failures in machines. What if you could predict when machines will fail? Predictive maintenance and AI/ML may help here if there are adequate conditions. Check it out in this RealPars video.
Time for Insights
The tricky part regarding predictive maintenance is that you need to have a good bunch of historic data with correctly labeled failure events. Otherwise algorithms will not be able to learn the conditions that caused that event. Also, sometimes failure events will be quite rare, adding to the difficulty of predicting them.
The initial investment in sensors, data infrastructure, and analytical software to perform predictive maintenance can be high, specially regarding brownfield equipment. Additionally, organizations need skilled personnel to interpret predictive data and act on insights effectively. Before starting any project with this kind of technology, a thorough analysis of benefits and expected ROI shall be performed. Starting small through a constrained pilot is advised.
Regarding algorithms, machine learning techniques, such as Random Forest and SVM, may classify equipment conditions, while unsupervised methods like K-Means detect anomalies. Time series models, including ARIMA and LSTM, may be used to predict future failures based on historical trends, and signal processing techniques like FFT can analyze mechanical vibrations for fault detection.