1. Introduction
Electric vehicles (EVs) have garnered significant attention due to their environmentally friendly characteristics and high efficiency [
1]. The motor serves as the core source of energy, enabling the movement of the electric vehicle. Permanent magnet synchronous motors (PMSMs) have been popularly used in the electric vehicles industries [
2]. Meanwhile, the inverter is responsible for controlling the motor’s speed and direction. The failure of the inverter, which is cause by the failure of power switches, could reach approximately 30% [
3,
4]. It is important for the open-circuit fault diagnosis system (OCFDS) to check the system to ensure it is able to work under normal and safe conditions [
5].
There are three main types of faults that can occur in motors: (1) mechanical faults, (2) electrical faults, and (3) magnetic faults [
6,
7,
8]. This paper mainly focuses on electrical faults, which can arise from stator phase winding short circuits, open circuits, ground faults, and other issues. Short circuits in the winding are usually caused by wire insulation breakdown, overheating, or overload [
9,
10]. This paper’s main focus is on open-circuit faults caused by driver problems, and proposed neural network-based open-circuit fault diagnosis system. Generally, the open-circuit fault diagnosis methods used for the inverter can be majorly classified into signal analysis processing methods, model-based methods, and data analysis methods [
11,
12]. Signal analysis processing methods use sensors to obtain the current or voltage in the phases for analysis [
13]. By using the measured current and voltage to compute the fault detection index, the system can check for faults [
14]. In [
15], current analysis was conducted to analyze fault and normal conditions. Subsequently, a conditioned table was created to represent the normal and faulty operation of the symmetrical components of the PMSM motor. The symmetrical components of the PMSM can be categorized into three types: (1) zero sequence, (2) positive sequence, and (3) negative sequence. To detect faults, the magnitude ratio of the positive sequence component to the negative sequence component was calculated to produce a fault detection index, which was utilized for fault type identification. Finally, the fault detection index was used in fault localization to determine the specific faulty switch. In [
16], the open fault in the open winding motor fault diagnosis system was based on the differential-mode component to identify faults in the PMSM system. Through current analysis under faulty conditions, the zero sequence current and zero sequence voltage in the differential mode component were computed [
16,
17,
18]. A zero sequence controller was employed to identify the fault type and perform a comparison of conditions to locate the faulty switch.
For the model-based method, the model was used to predict the outputs and compare them with the measured values to check and determine whether faults are occurring in the system [
16,
19,
20]. In Reference [
21], a Disturbance-Observer-Based model was used to estimate external disturbances and the unmodel dynamics and provide them to the torque reference. This system proves to be particularly effective in instances of torque fluctuations attributable to open-circuit conditions. Under such circumstances, the disturbance observer is responsible for determining the parameters necessary for the fault-tolerant switching table. This table plays a pivotal role in regulating the torque and stator flux, thereby improving the interference rejection ability of the system under open-circuit fault conditions. In Reference [
22], a hybrid diagnosis method was proposed; there, a Luenburger observer was used to obtain the current residuals for estimating the three-phase currents. Then, the principle current analysis (PCA) and the support vector machine (SVM) were used to evaluate the current residuals to locate the faults. PCA was used to reduce the computational load on the classifier and refine the dataset into more distinct sample types, while SVM was applied to categorize and identify the specific type of fault. In [
23], an adaptive sliding mode observers (SMOs) diagnosis method for detecting open-circuit faults in inverters used in PMSM drives was introduced. The comprehensive inverter system was decomposed into augmented and non-singular coordination transformations. SMOs were utilized to estimate the system state vector and calculate the residual error, enabling the determination of the fault’s phase location and the identification of the faulty switch. In [
24], an approach was introduced for diagnosing open-circuit faults in drives equipped with Model Predictive Current Control (MPCC). By utilizing MPCC, their model is capable of predicting currents and generating a corresponding cost function which the fault detection system uses for calculating a fault index. The subsequent stage involves a fault localization function that employs the calculated fault index to categorize the fault type based on predefined thresholds.
The data analysis method uses models based on data to analyze the conditions of the system and differentiate between normal states and various types of fault conditions. Several open-circuit fault diagnosis systems (OCFDS) based on machine learning methods have been developed for fault detection. In [
25], modular multi-level converters (MMCs) were analyzed, and specific conditions (such as the current and voltage passing through the phase) were categorized based on the type of faulty conditions. A neural network (ANN) was trained under normal operating conditions and three different types of faulty operating conditions. The fault diagnosis system successfully classified the three types of faults in single-submodule open-circuit faults. Next, a fault localization system was employed, leveraging the ANN’s output to determine the exact location of the faulty switches or capacitors. In [
26], an improved support vector machine (SVM) was utilized for open-circuit fault identification in the fault diagnosis system. This improved SVM utilized an Overlapped Wavelet Packet Transform (MODWPT), significantly enhancing the feature extraction process for fault identification. The SVM effectively classifies the faults, leading to an approximate 3% increase in accuracy compared to conventional SVM methods. This classifier was applied to determine faults within the submodule. In [
27], a model data hybrid-driven method was proposed. The artificial neural network was trained with two datasets: one comprising experimental or simulation data and the other containing data from the analytical model of the power converters. The model was designed to extract and analyze fault features present in both datasets, encompassing a classification scheme that recognizes seven distinct patterns, including normal operation. The diagnostic capability of the model allows for the precise identification of faulty switches by analyzing input diagnostic variables. In [
28], a 1D convolutional neural network (CNN) was trained using three-pole voltages under three different frequencies. The model was used to extract information on how variations in the modulation index and fundamental frequency influence pole voltages. A softmax function was used as its output layer, effectively classifying different types of faults occurring in submodule switches.
In traditional open-circuit fault diagnosis systems, the primary objective is to predict the phases that have experienced faults. However, the characteristics of multiple-phase motors are complex, involving considerations of intricate parameters. The current models have drawbacks, notably the lack of robustness and an inability to precisely identify specific faulty switches. This paper contributes in two significant ways: First, it implements neural networks into the open-circuit fault diagnosis system to enhance the accuracy in locating the faulty switch(es). Secondly, it determines an optimized neural network architecture suitable for application in the proposed open circuit fault diagnosis system.
5. Conclusions
In this study, two neural networks models, namely the CNN and CNN-LSTM models, were successfully implemented in the proposed open-circuit fault diagnosis system. Both models demonstrated high accuracy in detecting various faults across all three phases. Notably, both architectures achieved a similar detection performance. The experimental results confirm that the proposed system can achieve an overall detection accuracy of approximately 99.8%. In the first scenario, where a one-phase fault affected the high-side gate of Phase W, the fault was precisely detected after three oscillations of the Permanent Magnet Synchronous Motor (PMSM) at 2. In the second scenario, involving a two-phase fault affecting both Phase W and Phase V, the neural networks identified the faults at 8/3 for Phase W and 7/3 for Phase V. The delayed response of the Phase W neural network was attributed to fault signals closely resembling normal signals, resulting in a more gradual identification. In the third scenario, a three-phase fault impacting the low-side gates of Phase U and Phase V, along with the high-side gates of Phase V and Phase W, was concurrently pinpointed by all three-phase neural networks at 2. Overall this system effectively identifies the specific phase where the fault occurs, enabling the localization of faulty switches within the inverter.