Kalman-Cyclisation Dominant Frequency Based PMSM Bearing Fault Detection

Permanent Magnet Synchronous Motor (PMSM) as the key component of the Electric Vehicles (EVs), has numerous advantages over other synchronous motors. Fault diagnosis is essential to guarantee the safety and continuity of the operation for EVs. An undetected fault in the PMS M system may lead to high repair cost, or even catastrophic failure. Among lots of faults in the PMSM, the most serious are short circuit faults, such as inter-turn fault, phase-to-ground fault. If it is not detected in time, the inter-turn fault may lead to phase-to-ground fault. For the PMSM, the early detection of motor failure has a very important significance. In this paper, the mathematic model and the phase-to-ground fault model of PMSM in abc coordinates is proposed, and the corresponding model is established based on MATLAB /Simulink. The zero-sequence voltage component (ZSVC) is analyzed in the inter-turn fault, which provides the basis for the fault diagnosis of PMSM.

Polyphase permanent magnet synchronous motors (PMSMs) model outstanding performance in comparison to three-phase permanent magnet synchronous motors, and this is possible because of the recent development in power electronics. Five-phase permanent magnet synchronous motors have significant applications in aerospace and electric vehicles where highly accurate speed and torque control of the motor are a major requirement. This paper presents a detailed analysis of vector controlled five-phase permanent magnet synchronous motors in terms of efficiency, power, speed and torque ripple analysis along with the power factor analysis at different speeds. The average torque, torque ripple, speed, speed ripple and power factor for same are calculated and analyzed. The analysis has been carried out through MATLAB Simulink power graphical user interface.KeywordsPermanent magnet synchronous motorPhase sequenceTorque rippleSpeed rippleHybrid electric vehicle

Bearing failures represent the most prevalent fault type in electrical machines, potentially leading to catastrophic consequences if not detected early. Conventional detection methods primarily rely on thermal, acoustic, and vibration sensors. Traditional vibration-based techniques have gained widespread adoption due to their stable and straightforward signal-processing capabilities. However, these approaches require direct motor mounting, introducing economic, temporal, and safety inefficiencies. This study presents the first investigation of contactless radar-based detection of bearing faults according to the authors' knowledge. The research employs the absolute value of complex signals derived from quadrature signals recorded by a 24 GHz radar transceiver as the vibration signal. Various defects like corrosion, improper oil levels, and scratches were deliberately introduced to the inner race, outer race, and balls of bearings, establishing 16 distinct fault classes. Classification performance was evaluated using both time-domain statistical features and frequency-domain PSD features. Multiple machine learning algorithms were applied to both approaches, consistently achieving accuracy rates exceeding 98%. This study validates the potential of radar-based systems for bearing fault diagnosis and introduces a novel paradigm for contactless bearing fault detection comprising radar signal data from 880 experiments. The results demonstrate that radar technology offers a promising alternative to traditional contact-requiring methods, enabling efficient and reliable bearing fault classification through non-invasive vibration detection.

A vast amount of attentions has been given to permanent magnet synchronous motors (PMSM) in a variety of industrial applications due to recent developments in rare-earth permanent magnet (PM) materials and power electronics. These PM motors present numerous advantageous, such as a higher torque-to-inertia ratio and power density, over the other conventional machines used for ac servo drives. Applying the concepts of distributed control systems (DCS), wherein the control loops are closed through a network enables closing the slower speed control loop through a network. It consequently offers the capability of controlling many such systems from a single location at a lower installation and maintenance cost. This paper presents a study on the distributed control of the PMSM drive systems, using Ethernet as a remedy for common commercial fieldbus systems. However, the insertion of the non-deterministic communication network complicates the analysis and design as conventional control theories with many ideal assumptions, such as synchronized control, non-delayed sensing and actuation, must be re-evaluated. If these issues are addressed effectively, this state of the art concept may have promising applications in aero dynamic, naval, material processing etc., industries.

A light-gray-box simulation model is proposed for a multi-rotor propulsion chain designed to inject various faults into the system to generate fault behavior data sets. The model integrates the electrical and thermal characteristics of each component to model the interactions between components when faults are introduced. The simulated model is compared with an experimental test bench incorporating real hardware components, on which various faults can be injected to assess the accuracy of the simulated model. The propulsion chain consists of an Electronic Speed Controller, a Permanent Magnet Synchronous Motor, and a propeller. The faults considered are introduced on the three components of the propulsion chain. Electronic Speed Controller faults are applied to the power transistors; motor failures are open circuit, overheating and friction; and propeller failures are blade breakage and propeller loss. Using simulation of components with various failures, the model is designed to generate scalable, systematic, and reproducible behavior datasets for the development of fault detection systems which would eliminate the need for real hardware components.

<div class="section abstract"><div class="htmlview paragraph">Permanent Magnet Synchronous Motor (PMSM) is a favorite choice for traction applications because of their high power-to-weight ratio, torque-to-current ratio, high efficiency. In PMSM motors to perform the electronic commutation, resolvers are required to detect the rotor position. Resolvers are placed nearer to the end windings of the stator by considering the Mechanical Assembly and interfacing aspects. In high-power traction applications, due to higher current, there will be a significant influence of electric fields (E-fields) and electromagnetic fields (B-fields) on the rotor position sensor due to overhang components. The magnetic field induced by end-winding changes the excitation field, the magnitude of which decides the rotor angular position. This distortion of the excitation field will impact the sensing position and performance of the resolver. Analytical equations discussed in this paper also highlight that the machine output torque decreases with position error at higher speeds. The magnetic field in the overhang components of the winding is noticeably three-dimensional. In this research study, a 3D finite element simulation is performed to examine and suppress the impact of the B-fields and E-fields. In this approach, the influence of E-fields and B-fields is determined in radial and axial directions by creating planes. From this approach, shielding is provided for the resolver, due to that the effects of B and E-fields are suppressed by 88-95%. Due to the suppression, the resolver can deliver a precise rotor angular position for the motor.</div></div>

Bearing faults are the main contributors to the failure of electric motors. Although a number of vibration analysis methods have been developed for the detection of bearing faults, false alarms still result in losses. This paper presents a method that detects bearing faults and monitors the degradation of bearings in electric motors. Based on spectral kurtosis (SK) and cross correlation, the method extracts fault features that represent different faults, and the features are then combined to form a health index using principal component analysis (PCA) and a semisupervised k-nearest neighbor (KNN) distance measure. The method was validated by experiments using a machinery fault simulator and a computer cooling fan motor bearing. The method is able to detect incipient faults and diagnose the locations of faults under masking noise. It also provides a health index that tracks the degradation of faults without missing intermittent faults. Moreover, faulty reference data are not required.

An effective method for improving the signal-to-noise ratio from measurements of bearing housing vibration is presented.The method is based on the conventional adaptive noise cancelling (ANC) technique principle;it varies the main signal input and reference signal input.Since the main and reference inputs signals are collected at different times, this method is called asynchronous adaptive noise cancelling (AANC).It can improve the signal-to-noise ratio, as in the case of the detecting bearing faults when vibration signals of machinery are collected under low-frequency noise condition.In combination with AANC, statistical analysis, enveloping spectrum analysis techniques are used for detection of bearing fault and diagnosis.Experimental result has shown that these techniques can be made more effective only after AANC has reduced the background noise from the diagnostic signals.A new approach is proposed to the traditional fault detecting for self-aligning roller bearings when the shaft speed is low (250rpm) or high (1000rpm) condition.

Induction machines are commonly used in traction applications due to their robustness and lower production costs when compared to the Permanent Magnet Synchronous Motor (PMSM). In such traction applications, the induction machines are mainly operated by field oriented control. This control technique requires an accurate knowledge of the machine's parameters. The accuracy of the parameters influence the precision of the calculated rotor flux and its angle and the decoupling of the machine's equation into the direct and quadrature coordinate system (dq-components). Furthermore, the parameters can be used to configure the controllers of the field-oriented control system and therefore have an influence on the dynamic behavior and stability of the control. In this paper we will discuss, analyze and compare different methods to calculate the machine's parameters in an automated and rapid procedure with minimal measuring expenditure. Moreover, a method to configure a control that reduces the overall Ohmic losses of the machine in every point of operation will be presented and analyzed.

Detection of localised defects in rolling element bearings via composite hypothesis test

Application of acoustic emission technique for the detection of defects in rolling element bearings

Because of the benefits reduced size, cost and maintenance, noise, CO2 emissions and increased control flexibility and precision, to meet these expectations, electrical equipment increasingly utilize in modern aircraft systems and aerospace industry rather than conventional mechanic, hydraulic, and pneumatic power systems. Electric motor drives are capable of converting electrical power to drive actuators, pumps, compressors, and other subsystems at variable speeds. In the past decades, permanent magnet synchronous motor (PMSM) and brushless dc (BLDC) motor were investigated for aerospace applications such as aircraft actuators. In this paper, the fractional-order PID controller is used in the design of speed loop of PMSM speed control system. Having more parameters for tuning fractional order PID controller lead to good performance ratio to integer order. This good performance is shown by comparison fractional order PID controller with the conventional PI and tuned PID controller by Genetic algorithm in MATLAB soft wear.

Due to its straightforward installation, operation and low cost, the Induction Motor (IM) is widely used in industry. Because of its constant service, there is a growing necessity of fault detection techniques for IM. Bearings fault (BF) is a common fault that may appear in an induction motor, and it may cause severe damage to the device. Thus, early detection is necessary to program the corresponding maintenance and extend the service of the machine and to reduce expenses for maintenance service or replacement. In literature, different techniques have been proposed to detect this fault involving a signal preprocessing step and applying time or frequency domain approaches, which require time and resource consumption. In this paper, a technique to detect BF based on Motor Current Signal Analysis (MCSA) via a statistical analysis named Kolmogorov-Smirnov Test (K-S test), is presented. K-S test determines if two samples come from the same distribution by measuring the maximum distance between them. We compared signals of two motor conditions, no damage (ND) and BF. We applied the test to the raw current signal.

Interturn short-circuit fault is a kind of initial failure of the permanent magnet synchronous motor (PMSM) and serious consequences may be caused if it is left undetected. In this letter, a simple fault indicator is defined as the sum of the absolute values of amplitude differences between the stator currents, where the amplitudes of fundamental components of the stator currents are calculated by the frequency-tracking algorithm. Then, an online interturn short-circuit fault detection method is proposed for the PMSM. The experimental results validate the effectiveness of the proposed method.

Early detection of defects in bearings is essential to avoid the complete failure of machinery and the associated costs. This study presents a novel method for fault diagnosis of bearings using sensor fusion with a microphone and an accelerometer. The system has five modules, namely data acquisition, signal processing, feature extraction, classification and decision-making. A test-rig is designed to collect acoustic and vibration signals. Then, for each signal, indices are calculated in the time and frequency domains. After using principal component analysis (PCA) for feature extraction, the k-nearest neighbours (kNN) method is used in the classification module. Finally, a decision on the kind of fault and its size is made based on the decision fusion module. The aim of this study is to propose a fusion method to improve the effectiveness and reliability of bearing defect diagnosis compared to what can be achieved with vibration or acoustic measurements alone. The results obtained from this preliminary study show that condition monitoring using the accelerometer is the more effective technique for determining the type of fault, while the microphone is effective for classifying the size of fault. Experimental results also confirm these findings.