Detection and Diagnosis of Compound Faults in Induction Motors Using Electric Signals from Variable Speed Drives

As a primer driver, induction motors are the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal statues and avoid excessive energy consumption and severe damage to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar faults under closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives more correct indication of the fault severity and location for all operating conditions

Diagnosis of Compound Faults in Reciprocating Compressors Based on Modulation Signal Bispectrum of Current Signals

This paper studies induction motor current signatures to detect and di-agnose faults of a two-stage reciprocating compressor (RC) which creates a varying load to the motor. It also examines the influences of stator winding faults on differ-ent common faults of the compressor. Both the conventional spectrum analysis and the state of the art modulation signal bispectrum (MSB) analysis are used to process the current signals for attaining an accurate characterisation of the modulation in-duced by the variable loads and thereby developing reliable diagnostic features. The experimental studies examine different RC faults including valve leakage, inter-cooler leakage, stator asymmetries and their compounds. The results demonstrated that the MSB has a better performance in differentiating spectrum amplitudes caused by different faults especially the compound fault. Thus the MSB based fea-tures are demonstrated to be more reliable and accurate as the analysis techniques for motor current based diagnostics

Modulation signal bispectrum analysis of electric signals for the detection and diagnosis of compound faults in induction motors with sensorless drives

As a prime driver, induction motor is the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal status and avoid excessive energy consumption and severe damages to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar (BRB) faults under closed loop operation modes. It examines the effectiveness of conventional diagnostic features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives a more correct indication of the fault severity and its location for all operating conditions

Detection and Diagnosis of Compound Faults in a Reciprocating Compressor based on Motor Current Signatures

Induction motors are the most common driver in the industry and consume tremendous energy every year. Monitoring the status of a motor and its downstream equipment and diagnosing faults in time not only avoids great damage to mechanical systems but also allows the motor to run at optimal efficiency. This paper studies the use of information from motor current signals to detect and diagnose faults of a reciprocating compressor (RC) and its upstream three-phase motor. The motor is applied by the RC with an oscillator torque which induces additional components in measured current signals. Moreover, the current signatures contain changes with the torque profiles due to different types of faults. Based on these analytical studies, experimental studies were carried out for different common RC faults, such as valve leakage, intercooler leakage, stator asymmetries and the compounds of them. The envelope analysis of current signals allows accurate demodulation of the torque profiles and thereby it can be combined with overall current levels for implementing model based detections and diagnosis. The results show these simulated faults can be separated under all operating pressures

Information Extraction Based on the Analysis of Motor Supply and Structural Vibration for Machinery Condition Monitoring

Reciprocating compressors (RC) are one of the most widely used industrial machines due to their flexibility and reliability. Commonly exposed to harsh working environments, compressors experience various faults that affect their operational performance and functionality. Unfortunately, conventional condition monitoring methods such as vibration monitoring shows inefficiency sometimes in detecting multi-faults that occur in RCs even thoigh it needs a high-cost system for monitoring equipment. The AC motor driving the RC undertakes an oscillating torque which induces additional components in the measured current signals and changes with the presence of faults in either compressor or motor with consequent. Extensive investigations have shown that motor current signature analysis (MCSA) has the potential to be an accurate and cost-effective technique for the detection and diagnosis of common RC faults (i.e discharge valve leakage, intercooler leakage, broken rotor bars, stator winding asymmetry, and discharge valve leakage combined with stator winding asymmetry). However, a full study has not been found to consolidate this deduction. This research has adopted a model based simulation and experimental evaluation to verify and detail the use of MSCA for monitoring compressors under a wide range of load and fault conditions. The study firstly develops an extended and comprehensive dynamic model of the compressor, which links the various effects including mechanical dynamics, electrodynamics and fluid dynamics and, thus allows the current signature variations, particularly the sideband patterns to be simulated, and studied under common faults and their combinations at different severity. The model was validated against measured current signatures. Moreover, an advanced and effective technique, modulation signal bispectrum (MSB) has been identified to be the accurate tool as it can accurately extract the effect of sidebands and suppresses inevitable noise influences. Subsequently, a number of experiments were carried out based on a general purpose two-cylinder RC. Experimental results have shown that the seeded compressor faults caused observable changes in the motor current signature, inducing non-linear and modulation characteristics into the measured signal. The above-cited compressor faults generate different patterns and varying load on the motor, thereby inducing changes in modulation features in the current signal. Especially for the combined fault, an increase in sidebands can be observed by MSB analysis due to its high performance of noise reduction and nonlinear feature extraction. The MSB results in primary diagnostic features; sideband peaks at the first and second orders of compressor operating frequency has the ability to differentiate RC fault cases over a wide range of operating conditions, which allowed fault diagnosis without the need for additional measurements such as pressure, speed, or temperature. The successful detection and diagnosis of common compressor and motor faults through experiments and model developments confirm the capability of MSB based MCSA method. With the successful diagnosis of RC and motor faults, this research study is progressed to validate the capability of MSB based methods to diagnose different common compressor faults relating to compressor leakages, motor faults, and combined fault. The results show that MSB signatures allow accurate differentiation between normal condition and abnormal change induced by these seeded faults compared to conventional analysis, confirming the effeciancy of the signal processing technique proposed in this thesis