Abstract
Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.
Highlights
As of the past fifty years, highly technological methodologies have made it possible to monitor operating conditions, allowing for intelligent decisions about the maintenance interventions of plants or components, in any kind of industry, in order to achieve an effective maintenance
Jayaswal and Wadhwani, in 2009 [31], reviewed the techniques successfully implemented for the automated fault diagnosis of bearings until that time, and refer to expert systems developed with multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural network (PNN)
The results demonstrated that the performance of support vector machine (SVM) was significantly better in comparison to Fisher linear discriminant (FLD) and KNN
Summary
As of the past fifty years, highly technological methodologies have made it possible to monitor operating conditions, allowing for intelligent decisions about the maintenance interventions of plants or components, in any kind of industry, in order to achieve an effective maintenance. These are the well-known condition monitoring or predictive maintenance techniques, which significantly improve productivity, reliability, efficiency, and operating safety [1]. The faults that can be detected through vibration-based condition monitoring techniques in rotary machines are manifold; among them, looseness, eccentricity, unbalance, blade defects, misalignment, defective bearings, damaged gears, and cracked or bent shafts are some of the most investigated phenomena.
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