Predictive Maintenance (PdM) represents an innovative approach to maintaining and managing assets, moving away from traditional, time-based, or reactive strategies. Leveraging technologies like Artificial Intelligence (AI) and Machine Learning (ML), PdM predicts when equipment might fail, facilitating interventions at opportune times, thereby enhancing efficiency and safety while minimizing downtime. This article explores the technical aspects, uses, and some niche aspects of predictive maintenance.
Uses of Predictive Maintenance:
Predictive Maintenance prime application lies in industries with high-value equipment, where failures can lead to significant financial losses or safety hazards. These include industries like aviation, manufacturing, power generation, and transportation. A well-implemented PdM system can anticipate equipment failures, schedule maintenance tasks, reduce downtime, and even extend the lifespan of machinery. This can translate into huge cost savings, improved operational efficiency, and enhanced safety.
PdM leverages a myriad of ML techniques including supervised learning, unsupervised learning, and reinforcement learning. The system collects real-time data from multiple sensors embedded in equipment, such as vibration, temperature, pressure, and sound sensors. AI algorithms then process this data to identify patterns, trends, and anomalies that might indicate a potential failure. These algorithms could range from simple regression models to complex deep learning architectures, depending on the application.
Some of the niche aspects of Predictive Maintenance:
Imbalanced Datasets: PdM systems often grapple with imbalanced datasets, a challenge not as frequently encountered in other AI/ML domains. Failures in high-value equipment are generally rare, meaning the vast majority of collected data points represent the 'normal' functioning of the machinery, with a minuscule portion representing failures. This imbalance poses a challenge for ML algorithms as they are more likely to predict the majority class, i.e., the normal state. Techniques such as oversampling the minority class, undersampling the majority class, and using performance metrics sensitive to class imbalances (like the Area Under the ROC Curve) are often used to tackle this issue.
Emphasis on Feature Engineering: Another unique aspect of PdM is the strong emphasis on feature engineering. The raw sensor data often needs extensive preprocessing and transformation to be useful for ML models. For example, a sudden spike in vibration might be a more useful indicator of failure than the absolute vibration value. Hence, domain expertise is crucial to develop meaningful features that effectively capture the relevant information in the data.
The repo contains prebuild solutions for the following in form of notebooks
- RUL - Remaining useful life study of Electric Batteries
- Gas Turbine Pdm Study
- Predictive Maintenance Applied to Genesis Machines
- Predictive Maintenance Applied on Milling Machines datasets
- Predictive Maintenance Study on NASA Jet Engine degradation
- Predictive Maintenance applied on Building Telemtry system