Jing Tang

A condition-based maintenance decision-making framework for multi-component systems is proposed in this work by integrating dynamic Bayesian network (DBN) with proportional hazards model (PHM). The framework is designed to address the challenge of handling mixed failure types and complex failure dependencies, which often lead to inaccurate maintenance decisions in existing methods. In this integrated model, the DBN captures the failure evolution and both dynamic and static dependencies among components, while the PHM enhances the capability to characterize mixed failure interactions, thereby enabling the coverage of three common types of failure dependencies in multi-component systems. The model is formulated and solved using a finite-horizon Markov decision process (MDP), with the optimal maintenance strategy obtained by maximizing the total expected reward. Numerical case studies demonstrate the framework’s flexibility in handling mixed failures and complex dependencies, showing its potential to effectively support condition-based maintenance decision-making for complex multi-component systems.

The permanent magnet synchronous motor (PMSM) is used widely in electric vehicle application due to its high-power density and efficiency. Stator fault is a frequently fault in the motor as it usually works in a harsh environment. Therefore, a stator fault diagnosis method based on the offline motor parameter measurement is proposed to detect and evaluate the stator fault in this paper. Firstly, the line-to-line resistance and inductance of a healthy motor are analyzed when a DC voltage and a high-frequency voltage are excited to the motor respectively, where the DC and AC equivalent circuits at a standstill are introduced. Then, to analyze the resistance and inductance of the stator fault, an extra branch is added to the fault part to obtain the fault equivalent circuits. Accordingly, the stator fault resistance and inductance are derived, and then the resistance and inductance differences between healthy and fault motors are analyzed to provide the basis for the stator fault detection. Furthermore, the fault indicators are defined based on the resistance and inductance differences when a motor has a stator fault. Hence the stator fault severity and location can be evaluated by using these fault indicators. Finally, the experimental results from a 400 W permanent magnet synchronous motor are demonstrated to validate the proposed method.

The modeling of stator and rotor faults is the basis of the development of online monitoring techniques. To obtain reliable stator and rotor fault models, this paper focuses on dynamic modeling of the stator and rotor faults in real-time, which adopts a multiple-coupled-circuit method by using a winding function approach for inductance calculation. Firstly, the model of the induction machine with a healthy cage is introduced, where a rotor mesh that consists of a few rotor loops and an end ring loop is considered. Then, the stator inter-turn fault model is presented by adding an extra branch with short circuit resistance on the fault part of a stator phase winding. The broken rotor bar fault is then detailed by merging and removing the broken-bar-related loops. Finally, the discrete models under healthy and faulty conditions are developed by using the Tustin transformation for digital implementation. Moreover, the stator and rotor mutual inductances are derived as a function of the rotor position according to the turn and winding functions distribution. Simulations and experiments are performed on a 2.2-kW/380-V/50-Hz three-phase and four-pole induction motor to show the performance of the stator and rotor faults, where the saturation effect is considered in simulations by exploiting the measurements of a no load test. The simulation results are in close agreement with the experimental results. Furthermore, magnitudes of the characteristic frequencies of 2f₁ in torque and (1 ± 2s)f₁ in current are analyzed to evaluate the stator and rotor fault severity. Both indicate that the stator fault severity is related to the short circuit resistance. Further, the number of shorted turns and the number of continuous broken bars determines the rotor fault severity.

Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.