Mark Rayner

Low Order PWM Inverter Harmonics Contributions to the Inverter-Fed Induction Machine Fault Diagnosis

In this paper, the effects of inverter harmonics on motor current fault signatures are studied in detail. It is theoretically and experimentally shown that the fault signatures caused by the inverter harmonics are similar and comparable to those generated by the fundamental harmonic on the line current. Theoretically-derived extended relations including bearing fault, eccentricity, and broken rotor bar relations are found to match experimental results. Furthermore, it is observed and reported that the asymmetries on the rotor caused by broken rotor bars increase the amplitude of even harmonics. To confirm these claims, bearing, eccentricity, and broken rotor bar faults are tested and the line current spectrum of each faulty motor is compared with the healthy one. The proposed additional fault data are expected to contribute positively to the inverter-fed motor fault decision making algorithms.

Phase-Sensitive Detection of Motor Fault Signatures in the Presence of Noise

In this paper, a digital signal processor-based phase-sensitive motor fault signature detection technique is presented. The implemented method has a powerful line current noise suppression capability while detecting the fault signatures. Because the line current of inverter-driven motors involve low-order harmonics, high-frequency switching disturbances, and the noise generated by harsh industrial environment, the real-time fault analyses yield erroneous or fluctuating fault signatures. This situation becomes a significant problem when the signal-to-noise ratio of the fault signature is quite low. It is theoretically and experimentally shown that the proposed method can determine the normalized magnitude and phase information of the fault signatures even in the presence of noise, where the noise amplitude is several times higher than the signal itself. Since it has low computational burden, the developed algorithm is embedded to the motor control program without degrading drive performance. Therefore, it is implemented without any additional cost using readily available drive processor and current sensors.

DSP-Based Sensorless Electric Motor Fault Diagnosis Tools for Electric and Hybrid Electric Vehicle Powertrain Applications

The integrity of electric motors in work and passenger vehicles can best be maintained by frequently monitoring its condition. In this paper, a signal processing-based motor fault diagnosis scheme is presented in detail. The practicability and reliability of the proposed algorithm are tested on rotor asymmetry detection at zero speed, i.e., at startup and idle modes in the case of a vehicle. Regular rotor asymmetry tests are done when the motor is running at a certain speed under load with stationary current signal assumption. It is quite challenging to obtain these regular test conditions for long-enough periods of time during daily vehicle operations. In addition, automobile vibrations cause nonuniform air-gap motor operation, which directly affects the inductances of electric motors and results in a noisy current spectrum. Therefore, it is challenging to apply conventional rotor fault-detection methods while examining the condition of electric motors as part of the hybrid electric vehicle (HEV) powertrain. The proposed method overcomes the aforementioned problems by simply testing the rotor asymmetry at zero speed. This test can be achieved at startup or repeated during idle modes where the speed of the vehicle is zero. The proposed method can be implemented at no cost using the readily available electric motor inverter sensors and microprocessing unit. Induction motor fault signatures are experimentally tested online by employing the drive-embedded master processor (TMS320F2812 DSP) to prove the effectiveness of the proposed method.

Phase Sensitive Detection of Motor Fault Signatures in the Presence of Noise

In this paper, digital signal processor (DSP)-based phase-sensitive motor fault signature detection is presented. The implemented method has a powerful line current noise suppression capability while detecting the fault signatures. Because the line current of inverter driven motors involve low order harmonics, high frequency switching disturbances, and the noise generated by harsh industrial environment; the real-time fault analyses yield erroneous or fluctuating fault signatures. This situation becomes a significant problem when signal to noise ratio (SNR) of the fault signature is quite low. It is theoretically and experimentally shown that the proposed method can determine the normalized magnitude and phase information of the fault signatures even in the presence of noise, where the noise amplitude is several times higher than the signal itself.

Low-Cost Motor Drive-Embedded Fault Diagnosis - A Simple Harmonic Analyzer

The reference frame theory and its applications to fault diagnosis of electric machinery as a powerful tool to find the magnitude and phase quantities of fault signatures are explored in this paper. The core idea is to convert the associated fault signature to a dc quantity, followed by calculating the signal average value in the new reference frame to filter out the rest of the signal harmonics, i. e. its ac components. Broken rotor bar and rotor eccentricity faults are experimentally tested both offline using the data acquisition system, and online employing the TMS320F2812 DSP to prove the efficacy of the proposed tool. The proposed method has been theoretically and experimentally proven to detect the fault harmonics and determine the existence and the severity of machine faults. The advantages of this method include the following: (1) no need to employ external hardware or a PC running a high level program; (2) provides instantaneous fault monitoring using a DSP controller in real time; (3) embedded into the motor drive; thus, readily available drive sensors and the core processor are used without employing additional hardware; (4) no need to store machine currents data, and thus no need for large memory size; (5) very short convergence time capability; (6) immune to non-idealities like sensor dc offsets, imbalance, etc.

PWM Inverter Harmonics Contributions to the Inverter-Fed Induction Machine Bearing Fault Diagnosis

The effects of inverter harmonics on motor current fault signatures are studied in detail in this paper. According to theory and experimentation, the fault signatures caused by the inverter harmonics are similar and comparable to those generated by the fundamental harmonic of the line current. Unlike the utility-driven motor, monitoring the current of the inverter-fed motor is considerably noisy, which can mask the fault signatures and render a wrong fault warning. Therefore, the proposed additional fault data processing technique is expected to support the inverter-fed motor fault decision making algorithms effectively. The theoretically derived bearing fault relations are found to match the experimental results. In order to confirm these claims, outer race bearing faults are tested and the line current spectrum of the faulty motor is compared to the healthy one.

Fault diagnosis technique of induction machines with ordered harmonic and noise cancellation

In this paper, a fault diagnosis technique through fast Fourier transform (FFT) with suppressed the noise content is studied in greater detail. Because the line current utility driven motors involve interference components such as low order harmonics, high frequency disturbances, and the noise generated by harsh industrial environment, the fault analyses yield erroneous or fluctuating fault signatures. Mostly, the amplitudes of these components are higher than those of the fault signature itself and neither their existence nor behavior is predictable. The precision of the fault diagnosis methods mostly suffers from these high energy components and the ambiguity caused by them. Here, a new method is proposed to monitor and detect the fault signature through the noise ambiguity estimation of a motor current signal, which is cooperatively used to further enhance the fault diagnosis capability and reliability of the familiar FFT technique. The proposed algorithm is mathematically derived in detail and experimentally verified using a 3-hp motor generator setup.

Simple realtime condition monitoring tools for low-cost motor drives

In this paper, two simple digital signal processor (DSP)-based motor fault signature detection techniques are presented. First, the reference frame theory and its applications to fault diagnosis of electric machinery are introduced. Second, phase sensitive detection (PSD) of motor fault signatures is presented. Both techniques provide very simple and robust way to find the magnitude and phase of the specified fault signatures in the line current. Particularly, PSD has a powerful line current noise suppression capability while detecting the fault signatures. Various faults are experimentally tested both offline using the data acquisition system, and online employing the TMS320F2812 DSP to prove the efficacy of the proposed tools. The advantages of these methods include the following: (1) no need to employ external hardware or a PC running a high level program; (2) provides instantaneous fault monitoring using a DSP controller in real time; (3) embedded into the motor drive; thus, readily available drive sensors and the core processor are used without employing additional hardware; (4) no need to store machine currents data, and thus no need for large memory size; (5) very short convergence time capability; (6) immune to non- idealities like sensor dc offsets, imbalance, etc. ; (7) no need for a notch filter to filter out the fundamental harmonic; (8) steady state or stationary current signal assumptions are not necessary; (9) a familiar concept for motor control engineers; and (10) applicable to all multi-phase and single phase motors.