KEYWORDS: Transformers, Data modeling, Fourier transforms, Sensors, Signal processing, Detection and tracking algorithms, Signal detection, Signal to noise ratio, Neural networks, Feature extraction
Based on the development and practical application of transformer maintenance technology, this paper solves the core technical problems in on-line monitoring and fault diagnosis of transformer operating conditions. Though the integrated sensor of sound, vibration and temperature, the working conditions and operating state parameters such as sound, vibration and temperature of the transformer are collected. By using network communication to transmit data to the cloud platform for storage, multi-information fusion artificial intelligence modeling is performed on the collected operating conditions and operating status data to obtain an anomaly detection model. An anomaly detection model is deployed on the cloud platform to perform real-time detection on the data collected online, and if the system detects an abnormal state, an abnormality warning is performed. The short-time Fourier transform (STFT) and deep neural network are used to establish a relationship model between monitoring data and transformer faults, and the model is used to realize fault diagnosis.
Coal mills are important equipment of thermal power units work in hostile environment, so the health working condition of coal mills is a key factor for the normal operation of power plants. To monitor the operation of coal mills and make early warning of fault diagnosis, it has to collect the vibration/sound signals of coal mill operation and pre-process the sound samples to extract Mel-frequency cepstrum coefficients (MFCC) as features, and use the set of MFCC to train Gaussian mixture models (GMM). Finally, it uses the trained GMM to test the operation of coal mills. The test results demonstrate that audio anomaly detection based on MFCC and GMM can be used to effectively identify the on/off status of coal mills.
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