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Citations
The pump is an essential technical device used in almost all major sectors. For the undisturbed running of different sectors, the failure of the pumping system should be prevented. The failure of the pump causes loss of production, therefore, loss of revenue. These failures should be detected at an early stage to avoid catastrophic failure. Many traditional methods, such as vibration analysis and motor current signature analysis, can detect the fault after the failure occurs, but artificial intelligence (AI)-based techniques can identify the failures more efficiently to predict the fault early. Among various AI-based methods, machine learning (ML) and deep learning (DL)-based methods are the most useful techniques with widespread applications. This paper presents a comprehensive review of studies on various faults in centrifugal pumps and their identification by various traditional and ML-based techniques.
An effective asset management has a direct impact on maintenance costs, reliability, and equipment availability, especially in hydraulic machinery. Variable displacement axial piston pump is a major component used in the industry due to its load capacity ratio, pressure management, and high performance. Some of the main faults are wear and abrasion of the valve plates, increasing pressure losses as well as temperature, decreasing volumetric efficiency, and abnormal vibration. The off-line methodology implemented includes preprocessing of the vibration signals taken from the test bench available for this study, the feature extraction using wavelets, a stage of detection and classification through the use of artificial neural networks. Several networks were assessment, such as Adaline, nonlinear, and multilayer perceptron networks. Classification percentages greater than 90% are obtained taking into consideration 5 wear conditions related to the loss of volumetric efficiency.
