Signal processing methods in fault detection in manufacturing systems

The paper gives a short introduction to the problem of fault detection in manufacturing systems using digital signal processing methods. Usually, in manufacturing systems faults can occur in electrical drives, transmission lines, power management systems and can be detected through sensor data acquisition. Important task of the diagnosis is to differentiate normal operating condition from faulty condition. Detection of occurred faults in manufacturing systems depends on how efficiently erroneous features are extracted from acquired signals. This work focuses on signal processing based methods using Discrete Wavelet and Wavelet Packet Transforms for detection and classification the occurred faults. The faults are simulated using test signals with different time and frequency properties and the results obtained from different approaches are evaluated and compared. The simulation results prove that the proposed techniques handle the problem of fault detection and may even predict abnormalities exploring long term tendencies of the detected signals.