Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

Abstract

Abstract-A considerable amount of papers has been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper, a new method is proposed, which is based on robust statistical techniques applied in quality control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques, which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked: a broken bar, a faulty bearing, and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.

Vibration analysis of reconditioned high-speed electric motors

“…• Most systems and most diagnosticians report the damage and abnormalities associated with the rotor when they analyse the current spectrum around the supply frequency [16][17][18]. It is necessary to have a measuring system with a sufficiently low level of noise and large dynamics of measurement to correctly identify damage to bearings.…”

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

“…This method can detect the broken bars of a motor when it is fed in five different ways, directly to the power supply network and through four converters of various brands and models. García-Escudero et al [42] also present a method based on statistical techniques (Control Quality) to determine the early failures in inverter-powered motors. According to the authors, this method for running engines is a robust proposal for the detection of broken bars, mixed eccentricity, and bearings.…”

Fault detection in switching process of a substation using the SARIMA–SPC model

“…First, the time series model is used to analyze the characteristics of the switching behaviors of a substation, and thus to construct a suitable seasonal time series model, , to obtain the independent residual sequences that follow the same distribution. Second, the determined independent residual sequences are used to demonstrate a one-way control chart and a difference analysis is performed, ultimately discriminating the state of the substation 28 , 29 . This proposed method overcomes the challenges associated with the above conditions and the deficiencies of other methods.…”