Linear actuators play a critical role in industrial automation, powering a wide range of machinery and processes. However faults and malfunctions in these actuators can lead to costly downtime production delays and compromised operational efficiency. Optimized Artificial Intelligence (AI) algorithms enable predictive maintenance and can diagnose faults in real-time, enhancing reliability and productivity by minimizing unplanned downtime. AI-based diagnosis systems go beyond simple detection and can offer insights into the specific fault type and the root cause of the problem, enabling targeted and efficient troubleshooting by engineers and maintenance teams.
- This use case is based on the “Detection and Diagnosis of Faults in Linear Actuators” dataset from Cranfield University.
- The goal was to detect and classify 4 states of a linear actuator: normal, backlash, lack of lubrication, and spalling.
- Using data collected from a linear actuator, the dataset contains several .mat files that can be converted to .csv files (using the SciPy library).
- All files corresponding to the same behavior were concatenated to have only four files in the end: Normal.csv, Backlash.csv, LackOfLubrication.csv and Spalling.csv. As the dataset contains a lot of spalling data compared to the other classes, only half of the spalling data was used to ensure a more balanced dataset for the training.
- We then used NanoEdge AI Studio to create an N-class classification project based on these inputs, capable of classifying the state of the linear actuator.
Current sensor and Accelerometer.
4 classes of classification: Normal, LackOfLubrication, Backlash, and Spalling
Signal length: 6000
Data rate: 25 Hz
90.65% accuracy, 20.3 Kbytes of RAM, 184 Kbytes of Flash memory