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As one of the most promising techniques, magnetic flux-based condition monitoring of electrical machines, has gained significant traction in the past decade. One of the key enabling factor is the advancements in sensor technologies to measure both the airgap magnetic flux and stray magnetic flux around the motors with high accuracy. This study presents a comprehensive review of advances in flux sensors and the recent trends in magnetic flux-based diagnostic techniques for various faults and electrical machines. In this context, various magnetic sensor technologies suitable for measuring both the airgap flux and the stray magnetic flux are introduced. Each of the sensor technologies are discussed exhaustively, including their working principles and applications in electrical machine fault diagnostics. In the following sections, various flux-based diagnostic techniques are discussed for different types of common faults in various ac machines. Finally, the future trends are discussed briefly under the light of several applications and their impact. [ABSTRACT FROM AUTHOR]

This paper presents an silicon carbide (SiC) MOSFET health condition monitoring method based on the switching transient measurements. Specifically, the device's turn-on time is used as the aging precursor, and a detection circuit is developed with picosecond resolution to detect the variations of the turn-on time. For this purpose, power cycles with constant junction temperature swings are applied on sample devices to accelerate their thermally triggered degradation. Over the aging process, device parameters are measured periodically using automated curve tracer to observe degradation-related changes. Also, a double pulse tester is used to evaluate devices’ switching transient changes throughout aging. Based on these results, both the theoretical discussions and experimental results imply that device turn-on time is a viable aging precursor and can be used to monitor device health at system start-up. High-resolution capture module of readily available system microcontroller is employed to measure the turn-on time through a simple auxiliary interface circuit on a buck converter. Experimental results show that onboard device aging can successfully be achieved during systems start-up in the order of microseconds. [ABSTRACT FROM AUTHOR]

In this article, an in situ SiC mosfet degradation monitoring method using readily available converter sensors is presented. Device's drain-source resistance in saturation and ohmic regions are employed as aging precursors to, respectively, indicate die and package-related degradations. The presented findings in this article are essential for low-cost and practical early warning systems in power electronics applications. To verify the effectiveness of the proposed method, a number of power-cycling tests are conducted to analyze aging process and fatigue mechanism. Device static parameters are measured throughout accelerated aging tests and the physical causes behind them are discussed in detail. It is shown that the drain-source resistance within saturation region at low gate bias can be used to indicate die-related degradation. Similarly, at full positive gate bias, device on-state drain-source resistance can be used to detect package-related degradation. In order to find out optimal variables such as applied gate bias, pulse duration, etc., a comprehensive single pulse test is conducted and the results are reported. Finally, the proposed degradation monitoring method is experimentally validated in the context of a three-phase inverter considering different sensor configurations and load types. [ABSTRACT FROM AUTHOR]

Today, power conversion in automotive industry depends heavily on the reliable operation of insulated-gate bipolar transistors (IGBTs). Condition monitoring of IGBTs and reporting imminent faults to driver dashboard are critical for avoiding any fatal accidents. In this study, first, a comprehensive comparison is carried out between on-state collector–emitter drop ($V_{ce,on}$) with gate threshold voltage (Vth), as two reliable aging precursors. In order to enrich current understanding of IGBT aging prognosis, accelerated aging methods are applied under a wide variety of thermal stresses. Aging mechanisms and physical phenomena responsible for variation in both $V_{ce,on}$ and Vth have been methodically investigated. These explanations are also supported by detailed failure analysis. Based on the extensive result sets for tested samples from different manufacturers with different structural types, it is found that $V_{ce,on}$ show different trends at different stress levels, whereas under the same test conditions Vth provides more consistent and robust state-of-health information. Finally, an example experiment has been performed in which continuous $V_{ce,on}$ measurements coupled with Vth information has been used to provide more insight about the device aging as well as faults in an automobile's sub-systems. [ABSTRACT FROM AUTHOR]

This paper presents a detailed magnet defect fault detection analysis through fluxgate sensors by monitoring the leakage flux around permanent magnet synchronous motors. The flux spectra of electric machines contain direct and most critical information to monitor and characterize magnet defect faults and their progressions. In the mainstream diagnosis techniques based on phase current and back-EMF analysis, the fault corresponding signature characteristics may vary and cause misleading results depending on motor topology, winding configuration, number and location of defective magnets, and controller parameters. In this paper, it is shown that leakage flux analysis provides some superior results for magnet defect diagnosis. For this purpose, the fault patterns in the leakage flux spectrum are exhaustively analyzed at different torque/speed profiles. Simulation and experimental results show that the deployment of a direction sensitive fluxgate sensor in magnet defect fault detection yields very promising results both in time and frequency domain analyses. [ABSTRACT FROM AUTHOR]

This paper presents a study on the topology-dependent magnet defect fault signatures in permanent-magnet motors. A new analytical approach is introduced to characterize the fault signatures in stator back electromotive force (EMF) and current waveforms using magnetic equivalent circuit. Stator winding configuration, winding connection type and location of damaged rotor magnets are some of the physical properties affecting the fault signature characteristics. Several cases with different number of pole and slot are investigated through the proposed method. In addition, different winding connections (including star and delta connection), different winding configurations (including single and double layer, fractional and full coil pitch), and different magnet defect number and location are scrutinized. It is shown that there are some cases exhibiting different fault patterns than the ones obtained through well-known fault models defined in the literature. It is essential to take these discrepancies into account in order to avoid false alarms. In addition, it is observed that some of the fault signatures show up in the stator back EMF spectrum but not in the current spectrum due to location and severity of magnet defect, and design specs. Comparative 2-D finite-element simulations and experimental results justify the theoretical magnet defect fault analysis and show the efficacy of the proposed approach. [ABSTRACT FROM PUBLISHER]

The efforts for more reliable power conversion systems have been gaining momentum in recent years. The majority of the studies concerning reliability of power switches focus on the package-related failures, mainly caused by the cyclic thermal stress. The basic failure precursor for this type of stress has been identified as increased on-state resistance for power MOSFETs in the recent literature. On-state resistance monitoring during converter operation is a challenging and costly task as it requires current and voltage sensing circuits, which can block the high voltage across the switch during off-state to protect the measurement or control unit. This paper proposes a software frequency response analysis method to determine the health status of high-voltage power MOSFETs with high on-state resistance. This is achieved by analyzing and evaluating the variation in the plant model at double pole frequency using the same DSP that is used for control purposes. The proposed concept is analyzed for boost converter; however, it can be used to detect the on-state resistance variation in other types of converters operating in continuous condition mode (CCM). The proposed algorithm is embedded in a low cost DSP and experimentally verified on a dc/dc boost converter. [ABSTRACT FROM AUTHOR]

In this paper, magnet defect faults and their corresponding reflections on permanent-magnet (PM) motor stator variables are investigated. An analytical approach based on a partitioned magnetic equivalent circuit is developed to determine the influence of magnet defect faults on PM motor variables. Due to the flexibility of the proposed method, the effect of each rotor magnets on each stator coil can be calculated to obtain the induced back electromotove force under faulty cases and observe the fault-related signatures in the frequency spectrum. The proposed tool significantly reduces the computational burden and provides sufficient accuracy, which significantly eases to simulate several magnet fault scenarios and examine detailed topology dependence relations in shorter time. Different cases, including various numbers and locations of defected magnets, winding configurations, and crack direction effects, are studied to understand the magnet defect influences comprehensively. The experiments and simulations are carried out at different speeds and load conditions to fully characterize the fault signatures. Comparative 2-D finite-element simulations and experimental results justify the theoretical magnet defect fault analysis and show the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of a performance-oriented electric motors diagnostics in modern energy conversion system. With increased demand for electrical energy in world industries, the population of energy conversion devices such as generators/motors has greatly increased. As emerging and not being a mature enough technology in the application of renewable energy conversion or electric-drive transportation, the protection and diagnosis of electric motors have been extensively studied for safety and reliability. Meanwhile, motor phase currents commonly involve random noise components generated by harsh energy system environments, low- and high-order harmonics interferences caused by power inverters and fast switching devices, and various other design imperfections. Therefore, it is quite challenging to model the overall noise content and eliminate the disturbance while detecting motor fault signatures. Due to the inherent random variation of motor noise statistics, the noise model and elimination strategy should also be adaptively updated according to instantaneous noise conditions through which detection can be done with predefined performance expectation. Several successful solutions in the literature have managed to perform a diagnosis under certain noise conditions; however, a detailed performance and adaptability analysis covering arbitrary noise variation has not been satisfactorily addressed. This paper mainly deals with performance oriented threshold design strategies for fault signature detection utilizing the noise statistics of the motor phase current signal. The proposed solution is generalized to cover arbitrary Gaussian noise variations and derive the optimal form of the threshold that satisfies user's prior detection quality expectations. The mathematical derivations are proved through statistical theory and the experimental verifications are performed by using a 3-hp motor setup. [ABSTRACT FROM PUBLISHER]

The reference frame theory constitutes an essential aspect of electric machine analysis and control. In this study, apart from the conventional applications, it is reported that the reference frame theory approach can successfully be applied to real-time fault diagnosis of electric machinery systems as a powerful toolbox to find the magnitude and phase quantities of fault signatures with good precision as well. The basic idea is to convert the associated fault signature to a dc quantity, followed by the computation of the signal's average in the fault reference frame to filter out the rest of the signal harmonics, i.e., its ac components. As a natural consequence of this, neither a notch filter nor a low-pass filter is required to eliminate fundamental component or noise content. Since the incipient fault mechanisms have been studied for a long time, the motor fault signature frequencies and fault models are very well-known. Therefore, ignoring all other components, the proposed method focuses only on certain fault signatures in the current spectrum depending on the examined motor fault. Broken rotor bar and eccentricity faults are experimentally tested online using a TMS320F2812 digital signal processor (DSP) to prove the effectiveness of the proposed method. In this application, only the readily available drive hardware is used without employing additional components such as analog filters, signal conditioning board, external sensors, etc. As the motor drive processing unit, the DSP is utilized both for motor control and fault detection purposes, providing instantaneous fault information. The proposed algorithm processes the measured data in real time to avoid buffering and large-size memory needed in order to enhance the practicability of this method. Due to the short-time convergence capability of the algorithm, the fault status is updated in each second. The immunity of the algorithm against non-ideal cases such as measurement offset errors and phase unbalance is theoretically and experimentally verified. Being a model-independent fault analyzer, this method can be applied to all multiphase and single-phase motors. [ABSTRACT FROM AUTHOR]

In this paper, a complete cross-correlation-based fault-diagnostic method is proposed for real-time digital-signal-processor (DSP) applications that cover both the fault-monitoring and decision-making stages. In practice, a motor driven by an inverter or utility line is run at various operating points where the frequency, amplitude, and phase of the fault signatures vary unexpectedly. These changes are considered to be one of the common factors that yield erroneous fault tracking and unstable fault detection. In this paper, the proposed algorithms deal with the ambiguities of line-current noise or sensor-resolution errors and operating-point-dependent threshold issues. It is theoretically and experimentally verified that a motor fault can be continuously tracked when the sensor errors are within a limited range through the adaptively determined threshold definition of noise conditions. The offline experiments are performed via Matlab using actual line-current data obtained by a data-acquisition system. These results are verified on a DSP-based motor drive in real time where drive sensors and a digital signal processor are employed both for motor-control and fault-diagnostic purposes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]