Novel Fast and Automatic Condition Monitoring Strategy Based on Small Amount of Labeled Data

Signal-based automatic condition monitoring techniques are one of the effective ways to ensure the operational safety of modern industrial systems. Currently, the main challenge is to increase their effectiveness under noisy environment and with limited labeled data. In this article, a novel fast and automatic signal-based fault diagnosis procedure that does not require any kinds of machine learning algorithms is proposed. This procedure is based on the measurement of the similarity in the frequency domain between the collected data and a small amount of labeled reference signals (LRSs). The LRSs are obtained under various known operation conditions using fast Fourier transform (FFT) algorithm. The proposed approach is suitable for a real-time implementation and capable of successfully overcoming the challenge of condition monitoring of rotating machines under noisy environment and with limited labeled data. The merits, fastness, and also robustness against noise of the proposed technique are demonstrated experimentally through different practical applications, as well as compared to state-of-the-art procedures on a database of vibration signals measured under a variety of machine health and working conditions.