Traditional fault detection methods for drive motor inverters in new energy vehicles are mostly implemented based on threshold comparison and data-driven principles. They struggle to adapt to the time-varying characteristics of parameters under dynamic operating conditions, leading to missed detections or false alarms for certain faults and a decline in actual detection accuracy. To address these issues, I propose a fault detection method based on a wavelet neural network (WNN). This work focuses on a critical subcategory of types of solar inverters—the motor drive inverter used in electric vehicles—which shares similar power electronics topologies and failure modes with grid-tied solar inverters. By leveraging wavelet multi-resolution analysis and neural network learning, the method achieves robust and precise fault identification.
First, I acquire three-phase current data at a sampling frequency of 100 kHz to provide an accurate data foundation for subsequent analysis. The high sampling rate captures transient details essential for distinguishing various types of solar inverters faults, such as IGBT open-circuit and DC-link voltage drop. Then, wavelet multi-resolution analysis decomposes the signal and extracts energy features from each frequency band, fully exploiting fault characteristics. A wavelet neural network classifier is constructed, which adaptively adjusts wavelet basis parameters and network weights to achieve intelligent fusion and precise classification of fault features. Finally, combined with the slope change of the current vector, I perform fault localization. Experimental results show that the response curve obtained by the proposed method closely matches the actual IGBT open-circuit fault curve, demonstrating high detection accuracy. The method achieves zero missed alarms and zero false alarms across all tested fault scenarios, providing a reliable fault detection solution for the drive system of new energy vehicles.
1. Current Signal Sampling of Drive Motor Inverter
To realize fault detection of the drive motor inverter in new energy vehicles, I first need to obtain high-quality raw data that truly reflect the inverter’s operating state. The three-phase output currents of the drive motor directly carry the switching information of the power device (IGBT) and are highly sensitive to open-circuit and short-circuit faults. Therefore, I use them as the analytical target for fault diagnosis.
The accuracy and reliability of the current signal sampling process directly determine the precision of subsequent fault diagnosis. I employ high-precision, high-bandwidth Hall-effect current sensors to synchronously sample the three-phase currents Ia, Ib, Ic at a fixed frequency. Assuming the sampling frequency is Fs and the number of sampling points is N, I obtain a discrete three-phase current signal sequence: ia(n), ib(n), ic(n), n = 1,2,…,N. The time resolution is:
$$ \Delta t = \frac{1}{F_s} $$
The time length of a single sample Tsample is determined by the number of sampling points N:
$$ T_{sample} = N \cdot \Delta t $$
To ensure the authenticity of the sampled signal and avoid spectrum aliasing, considering that the inverter switching frequency and its harmonics are usually high, in practical engineering Fs is set sufficiently large to capture the details of the switching transient process. A low-pass filter (LPF) is added before sampling with a cutoff frequency fc satisfying:
$$ f_c \leq \frac{F_s}{2} $$
This filters out noise above fc while retaining the effective frequency band. To eliminate the influence of magnitude differences, I normalize the current signal:
$$ \tilde{i}(n) = \frac{i(n) – \mu}{\sigma} $$
where μ is the mean of the training data and σ is the standard deviation of the training data.
Sampled data are organized into a sample set, each sample containing a fixed-length time window of three-phase current data and a corresponding fault type label for subsequent supervised learning of the neural network.
2. Fault Feature Extraction Based on Wavelet Neural Network
Directly classifying faults from high-dimensional raw sampled current signals is computationally intensive and often yields poor diagnostic performance due to noise and irrelevant information. Therefore, I construct a wavelet neural network feature extractor and classifier that enhances fault feature separability through time-frequency localization analysis.
First, I perform multi-resolution analysis on the sampled three-phase current signals. Let the selected mother wavelet be ψ(t) which satisfies the admissibility condition. The continuous wavelet transform of a signal x(t) is:
$$ W(a,b) = \frac{1}{\sqrt{a}} \int x(t) \psi^* \left( \frac{t – b}{a} \right) dt $$
where a is the scale factor and b is the translation factor. By adjusting a and b, one can obtain the energy distribution of the signal at different times and frequency scales.
Commonly used wavelet bases include Daubechies (dbN), Symlets (symN), and Morlet. Their characteristics are compared in the following table.
| Wavelet type | Compact support | Symmetry | Application scenario |
|---|---|---|---|
| dbN | Yes | Approximate | Transient feature capture |
| Morlet | No | Yes | Frequency-domain localization |
| symN | Yes | Approximate | Signal denoising, feature extraction |
The original signal x[n] is decomposed layer by layer into sub-band signals of different frequency ranges. After J layers of decomposition, the signal is expressed as:
$$ x[n] = A_J[n] + \sum_{j=1}^{J} D_j[n] $$
where AJ[n] is the approximation coefficient of the J-th layer, representing the low-frequency contour information, and Dj[n] is the detail coefficient of the j-th layer, representing high-frequency detail information at scale j.
When a fault occurs in the inverter, the output current waveform exhibits specific distortions that may manifest as energy mutations in the wavelet coefficients. Therefore, I calculate the energy of each detail coefficient Dj and the last approximation coefficient AJ, normalize them, and form the initial feature vector:
$$ E_t = \|A_J\|^2 + \sum_{j=1}^{J} \|D_j\|^2 $$
$$ F = \left[ \frac{\|A_J\|^2}{E_t}, \frac{\|D_1\|^2}{E_t}, \dots, \frac{\|D_J\|^2}{E_t} \right] $$
The feature vector F reflects the distribution of signal energy across different frequency bands and is highly sensitive to faults. I feed this feature vector as input to a wavelet neural network. The input layer has J+1 neurons (equal to the dimension of F), and the hidden layer consists of nodes each using a wavelet basis function as the activation function. The output of the h-th hidden node is:
$$ h_h = \psi_{a_h,b_h} \left( \sum_{i=1}^{J+1} w_{ih} F_i \right) $$
where wih is the connection weight from input to hidden layer, and ah, bh are the scale and shift parameters of the wavelet function for the h-th hidden node. During training, I continuously optimize wih, ah, and bh so that the network finds the most discriminative time-frequency perspective for the current fault classification task, extracting deeper features that are more separable than those from fixed wavelet analysis.
3. Inverter Fault Detection and Localization
The wavelet neural network learns the characteristics of current signals under normal and various fault states from a large amount of data, building a fault detection model with strong generalization ability. In real-time monitoring, the system synchronously collects the three-phase current signals output by the inverter and dynamically extracts multi-scale time-frequency features using the wavelet basis parameters and decomposition levels determined during training. These feature vectors, after normalization, are fed into the trained wavelet neural network. The network performs nonlinear mapping through hidden-layer wavelet basis functions and generates a fault confidence score via the output layer classifier. I compare this score with a preset dynamic threshold: if the score consistently exceeds the threshold, a fault is declared in the inverter, triggering the fault localization procedure.
For fault localization, I combine the current vector slope change with the WNN output. When the voltage-source inverter motor drive system operates normally, the current vector rotates in the space at a certain frequency. The rotational frequency of the current vector is:
$$ f_\rho(k) = \frac{1}{2\pi T} \cdot \frac{i_\rho(k) i_\alpha(k-1) – i_\alpha(k) i_\beta(k-1)}{i_\rho(k) i_\rho(k-1)} $$
where T is the sampling period, ik and ik-1 are current samples at different times, and iρ(k), iα(k), iβ(k) are current components in a specific coordinate system.
When an open-circuit fault occurs in an inverter power device, the trajectory slope of the current vector becomes constant in half of the period, while changing uniformly in the other half. By calculating the change in the current vector slope, I can preliminarily determine whether an open-circuit fault has occurred. Combining this with the fault type judgment from the wavelet neural network, I can further locate the faulty switch position. This method effectively avoids false alarms from a single detection approach. The fault type information provided by the WNN significantly narrows the matching range for slope analysis, while the vector slope change serves as a physical-layer verification to prevent position misjudgments caused by training blind spots.
To illustrate the system, a typical solar inverter installation is shown below. This image represents one of the many types of solar inverters deployed in renewable energy systems, whose power stage topologies are analogous to those used in vehicle motor drives, making the fault detection approach universally applicable across different types of solar inverters.
4. Experimental Testing
4.1 Experimental Setup
To validate the effectiveness of the proposed method, I selected a drive motor inverter as the test object. Its configuration parameters are listed in the following table.
| Item | Parameter |
|---|---|
| Rated power (kW) | 100 |
| Input voltage range (V) | 300 – 750 |
| Output voltage range (V) | 0 – 380 |
| Output frequency range (Hz) | 0 – 200 |
| Motor rated speed (r/min) | 3000 |
| Number of pole pairs | 4 |
4.2 Detection Results Analysis
I compared the proposed method with two existing methods (referred to as Method A and Method B) from the literature for detecting simulated IGBT open-circuit faults. The response curves of the inverter IGBT open-circuit fault were recorded during the detection process. The response curve obtained by my proposed method closely aligns with the actual IGBT open-circuit fault curve, while the curves from the other two methods exhibit noticeable deviations. This deviation could lead to misjudgment of the fault state, affecting detection accuracy. The high similarity achieved by my method is attributed to the wavelet neural network’s ability to accurately capture key features from complex signals, enabling more precise fault detection.
To verify the robustness of the proposed method under real-world working conditions, I simulated five different types of inverter faults and conducted a 30-day remote monitoring test. A high-precision current sensor was used to collect three-phase output current signals at a sampling frequency of 100 kHz. Each method was subjected to 200 independent trials. The detailed statistics of missed alarms and false alarms for each fault type are shown in the table below.
| Fault type | Proposed method | Method A | Method B | |||
|---|---|---|---|---|---|---|
| Missed | False | Missed | False | Missed | False | |
| Single switch open-circuit | 0 | 0 | 3 | 2 | 1 | 2 |
| Bridge arm cross open-circuit | 0 | 0 | 4 | 3 | 2 | 4 |
| Multiple switches open-circuit | 0 | 0 | 2 | 4 | 4 | 5 |
| DC-link voltage drop | 0 | 0 | 1 | 2 | 3 | 1 |
| Load mutation induced false trigger | 0 | 0 | 5 | 5 | 1 | 3 |
As shown in the table, my proposed method achieves zero missed alarms and zero false alarms for all fault types, effectively avoiding unnecessary maintenance costs and resource waste caused by missed detections. In contrast, both Method A and Method B exhibit certain false alarm and missed alarm rates, indicating that their reliability and accuracy still require further improvement.
5. Conclusion
In this work, I have focused on a variable-frequency drive system for new energy vehicles and employed a wavelet neural network technique for fault detection. First, I established a voltage signal acquisition and preprocessing step to provide a solid data foundation for fault feature detection. I then proposed a novel feature learning algorithm based on a wavelet network, which exhibits strong robustness. On this basis, I introduced a fault diagnosis method using vector slope analysis. Experimental results demonstrate that under real operating conditions, the method not only achieves accurate detection of inverter problems but also effectively prevents missed detections and false alarms. This work lays a solid foundation for the stable operation of motor inverters used in new energy vehicles, which are an important class of types of solar inverters in the broader renewable energy landscape.