Cyclostationary modelling of rotating machine vibration signals

Increasing interest has been seen in condition monitoring (CM) techniques for electrical equipment, mainly including transformer, generator, and induction motor in power plants, because CM has the potential to reduce operating costs, enhance the reliability of operation, and improve power supply and service to customers. Literature is accumulated on developing intelligent CM systems with advanced practicability, sensitivity, reliability, and automation. A literature survey is felt necessary with an aim to reflect the state-of-the-art development in this important area. After introducing the concepts and functions of CM, this paper describes the popular monitoring methods for and research status of CM on transformers, generators, and induction motors, respectively. The paper also points out the potential benefits through the utilization of advanced signal processing and artificial intelligence techniques in developing novel CM schemes.

Condition monitoring techniques for electrical equipment-a literature survey

Effective vibration analysis of ic engines using cyclostationarity. part i-a methodology for condition monitoring

Envelope order tracking for fault detection in rolling element bearings

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and diagnosis of the gear condition.

/pdf/gear-fault-detection-and-diagnosis-under-speed-up-condition-46xm1mzz06.pdf

J. Zhu

Papers

Monitoring varying speed machinery vibrations—I. The use of non-stationary recursive filters

Monitoring varying speed machinery vibrations—II recursive filters and angle domain