# Open-circuit fault detection in a 5-level cascaded H-bridge inverter using 1D CNN and ISTM

Chouaib Djaghloul, Kambiz Tehrani, François Vurpillot

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Chouaib Djaghloul, Kambiz Tehrani, François Vurpillot. Open-circuit fault detection in a 5-level cascaded H-bridge inverter using 1D CNN and ISTM. Energies, 2025, 18 (18), pp.5004. 10.3390/en18185004. [hal-05283279](https://hal.science/hal-05283279v1)

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Article

# Open-circuit fault detection in a 5-level cascaded H-bridge inverter using 1D CNN and LSTM

Chouaib Djaghloul <fnref n="1" />,<fnref n="2" /> ID, Kambiz Tehrani <fnref n="2" /> and François Vurpillot <fnref n="1" />

**Abstract:** It is well known that the power converters have the highest failure rate in the energy conversion chain in different industrial applications. This could definitely affect the reliability of the system. The reliability of converters in power conversion systems is crucial, as failures can lead to critical consequences and damage other system components. Therefore, it is important to predict and detect failures and take corrective actions to prevent them. One of the most common types of failure in power converters is semiconductor failure, which can manifest as an open circuit or a short circuit. This paper focuses on single and double open-circuit switch failures in a 5-level cascaded H-bridge inverter, for which a fast, precise method is required. A data-driven approach is employed here, using the output voltage and voltages across each H-bridge as diagnostic signals. A 1D-CNN LSTM neural network is trained to accurately detect and localize open-circuit faults, providing a reliable, practical solution.

**Keywords:** Power converters reliability; Multilevel inverter; Fault diagnosis; Switch fault diagnosis; Open-circuit fault detection; Neural network; 1D-CNN LSTM network.

## 1. Introduction

Multilevel inverters are widely used in power conversion systems due to their capacity outputs with lower total harmonic distortion and higher power quality. There are three main topologies of multilevel inverters [1]: Cascaded H-bridge Inverters (CHBI), neutral point clamped (NPC), and flying capacitor (FC), with CHBI being the most desired MLI topology due to its simplicity, modularity, and controllability [2]. A CHB MLI is comprised of a series of H-bridge connected in series, allowing for reach higher staircase voltage waveform. One of the drawbacks that the CHB MLI has is the high number of semiconductors used which reduce the reliability of these inverters the more the switches the more the inverter is prone to failures, and with the demand of many major application such as adjustable speed drivers, and renewable energy systems of a continuous not interrupted operation of the inverter [2], for that fault tolerant operation is necessary, but before that the failures must be detected first and located. Two main types of failures can affect power semiconductors: short-circuit failure and open-circuit failure [3]. Short-circuit failures have severe consequences on the inverters and are usually detected and prevented by some hardware circuit design, usually drivers, and they can also be transformed into open circuits by connecting fast fuses to the circuit [4]. Open-circuit failures which commonly arise from drive circuit malfunctions or the separation of solder wires [5] are not as severe as SC failures the system can continue to operate but with degradation in the output waveform

Received:

Revised:

Accepted:

Published:

**Citation:** Lastname, F.; Lastname, F.; Lastname, F. Title. *Energies* 2025, 1, 0. https://doi.org/

**Copyright:** © 2025 by the authors. Submitted to *Energies* for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Univ Rouen Normandie, INSA Rouen Normandie, CNRS, Normandie Univ, GPM UMR 6634, F-76000 Rouen, France.</footnote>

<footnote n="2">² University of Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France.</footnote>

<footnote>* Correspondence: kambiz.tehrani@uca.fr or kambiz.tehrani@ieee.org</footnote>
which may cause problem in circuit or in the load (heating, torque reduction...) [6], that's why many researchers focus on open-circuit fault detection in power converters.

Fault detection methods can be classified into 3 categories [5]: model based [7–9] where the fault is detected by comparing estimated values with measured values, signal based [10–13] where the fault is detected by directly extracting features from the signals (voltage, current), the last method is data based [14–22] where certain classifiers are used to detect and locate failures. Lately, there's been a trend where artificial intelligence algorithms are used to detect and identify failures, to avoid the drawbacks of model-based and signal-based algorithms, as the use of mathematical models and signal processing techniques can be complex and time-consuming. Several machine learning and deep learning algorithms have been applied for open-circuit fault detection in MLI; some of them are more traditional, and they rely on manually extracting features from signals (voltage, current...), for example, support vector machines, decision trees, random forests, and K-nearest neighbors. And other methods automatically learn features from raw signals, making them better options in terms of speed of detection, for they don't need complex preprocessing, for example: 1D Convolution neural networks (1D CNN), Long Short-Term Memory networks (LSTM) [23].

In [14], a neural network-based fault diagnosis system was developed for a three-phase Permanent Magnet Synchronous Motor (PMSM) driven by an inverter. Using phase-to-phase voltage data, the authors applied CNN and CNN-LSTM models to accurately detect and locate open-circuit faults in power switches, achieving an overall accuracy of approximately 99.8%. The system demonstrated strong performance in identifying one-, two-, and three-phase faults across different scenarios. In [15], Abas et al. proposed a current-bias-based diagnosis method for open-circuit faults in three-phase voltage source inverters using only current and rotor position signals, demonstrating robustness under various conditions. In [17], Yi Lu Murphey et al. (2006) used a feedforward neural network to detect switch failures to avoid the possible malfunction of motor drives due to open or short circuit failures in the inverter. The method was validated through simulation and experimental testing, which has proven it to be a more robust and more generalized method for fault detection with simple implementation. In [16], Surin Khomfoi et Tolbert Leon M. (2007) used Fast Fourier Transform (FFT) and Neural Network (NN) to classify switch failures in a 5-level Cascaded H-bridge Inverter (CHBI) to increase the reliability of inverters and avoid system shutdown once there's a failure. The FFT is used to extract features from the diagnosis signals, and then those features become the input of the NN, which classifies the failures. In [18], Lei Koua et al. (2020) used wavelet transform and deep forward neural networks to detect open circuit fault detection in full bridge DC-DC converters, taking as inputs current and voltage measurements. The method was tested by both simulation and experimental setup and had an accuracy of 97%. In [19], Lei Kou et al. (2020) developed a fault diagnosis method for open-circuit fault detection in 3-phase voltage source rectifiers based on a deep feedforward network with transient synthetic features. This approach does not require any additional sensors and has been demonstrated to achieve an accuracy of over 97% in an experimental setup. In [20], Yang An et al. (2022) proposed a method for IGBT open circuit fault detection in MMC, where a modified Elman neural network is used to compare the difference between the estimated and true value of bridge arm current. The method proved to be robust and has high accuracy. In [21] A. Sivapriya1 et al. (2023) presented an MLP-ANN-based method for fault detection and tolerance in single-phase CHB inverters under various open-circuit fault (OCF) conditions. The system accurately identifies single and double switch faults within 8–16 ms, achieving 96% accuracy. In [22], Shaishai Zhao et al. (2025) used a lightweight two-channel deep network (LTCDN) that takes as inputs the three-phase current of the inverter to detect open-circuit failures in 4L-ANPC. Experimental results indicate a fast and high level of
accuracy with less than 1% false detection within 8 Ms it was also applied on a cascaded H-bridge Inverter and demonstrated good accuracy.

In this work, an LSTM and 1D CNN are combined to get the benefit of both types: CNN extracts local features, and LSTM analyzes these extracted features over time to track changes in the signal and classify faults accurately. The output voltage and the voltage across each bridge of the MLI are used as signal diagnoses. The contributions of this work are the following:

* Reducing the number of signals used for the detection and localization of failures;

* The exact identification of faulty switches in inverters with single and double switch failure with high accuracy and a robust performance under varied conditions.

The rest of this paper is organized as follows: In section 2 the operation of a 5-level CHBI is explained, in section 3 the methodology for OCF detection is detailed, in section 4 the method was tested through simulation, and finally the paper is concluded in section 5.

## 2. Operation of 5-level CHBI

Figure 1 shows the topology of a 5-level Cascaded H-bridge Inverter, which consists of two H-bridges connected in series with a total of 8 switches, 2 identical voltage sources, and 8 anti-parallel diodes. For a CHBI, the number of possible levels of voltages is $2*n+1$, where n is the number of H-bridges [24].

<figure><img src="image_2.png" /><figcaption>Figure 1. 5 level CHB Inverter Topology.</figcaption></figure>

Figure 2 shows all the possible voltage levels in CHBI, and Table 1 presents the combinations of switch states required to generate each voltage level.

<table><caption>Table 1. Switch states corresponding to output voltage levels.</caption><thead><tr><th>Output Voltage</th><th>S1</th><th>S2</th><th>S3</th><th>S4</th><th>S5</th><th>S6</th><th>S7</th><th>S8</th></tr></thead><tbody><tr><td>2V<sub>dc</sub></td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>V<sub>dc</sub></td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td></tr><tr><td>0</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td></tr><tr><td>-V<sub>dc</sub></td><td>0</td><td>1</td><td>1</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td></tr><tr><td>-2V<sub>dc</sub></td><td>0</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>1</td><td>0</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 2. The output voltage waveform of CHBI.</figcaption></figure>

In order to generate the commands for each switch, several methods can be used; the most commonly used one is Sinusoidal Pulse Width Modulation (SPWM) [25], where a sinusoidal reference signal is compared with a carrier (usually triangular) signal. This method has two variants: Phase Shifted PWM (the carriers are phase-shifted) and Level-Shifted (the carriers are stacked). In this work, the Level-Shifted PWM was used as illustrated in Figure 3.

<figure><img src="image_4.png" /><figcaption>Figure 3. Level-shifted PWM.</figcaption></figure>

With 8 switches, failures can occur in any of them, in this work single switch and double switch failure is considered, triple switch failure is considered because it is very unlikely, under the assumption of independent switch device failure (with a probability of P«1) the probability of three simultaneous open-circuit faults in an CHB inverter with 8 switches is given by:

$$P_{3\text{-fault}} = \binom{8}{3} \cdot P^3 = \frac{8!}{3! \cdot 5!} \cdot P^3 = 56 \cdot P^3 \quad (1)$$

where $\binom{8}{3}$ represents the number of ways to select 3 switches from 8. Table 2 shows all the fault states with the corresponding label.
<table><caption>**Table 2.** Labeling of open-circuit fault (OCF) conditions for single and double switch failures.</caption><thead><tr><th>Number of Switches</th><th>Switches Affected</th><th>Label</th></tr></thead><tbody><tr><td>No switch failure</td><td>-</td><td>"1"</td></tr><tr><td rowspan="8">Single switch failure</td><td>S1</td><td>"2"</td></tr><tr><td>S2</td><td>"3"</td></tr><tr><td>S3</td><td>"4"</td></tr><tr><td>S4</td><td>"5"</td></tr><tr><td>S5</td><td>"6"</td></tr><tr><td>S6</td><td>"7"</td></tr><tr><td>S7</td><td>"8"</td></tr><tr><td>S8</td><td>"9"</td></tr><tr><td rowspan="20">Double switch failure</td><td>S1, S2</td><td>"12"</td></tr><tr><td>S1, S3</td><td>"13"</td></tr><tr><td>S1, S4</td><td>"14"</td></tr><tr><td>S1, S5</td><td>"15"</td></tr><tr><td>S1, S6</td><td>"16"</td></tr><tr><td>S1, S7</td><td>"17"</td></tr><tr><td>S1, S8</td><td>"18"</td></tr><tr><td>S2, S3</td><td>"23"</td></tr><tr><td>S2, S4</td><td>"24"</td></tr><tr><td>S2, S5</td><td>"25"</td></tr><tr><td>S2, S6</td><td>"26"</td></tr><tr><td>S2, S7</td><td>"27"</td></tr><tr><td>S2, S8</td><td>"28"</td></tr><tr><td>S3, S4</td><td>"34"</td></tr><tr><td>S3, S5</td><td>"35"</td></tr><tr><td>S3, S6</td><td>"36"</td></tr><tr><td>S3, S7</td><td>"37"</td></tr><tr><td>S3, S8</td><td>"38"</td></tr><tr><td>S4, S5</td><td>"45"</td></tr><tr><td>S4, S6</td><td>"46"</td></tr><tr><td>S4, S7</td><td>"47"</td></tr><tr><td>S4, S8</td><td>"48"</td></tr><tr><td>S5, S6</td><td>"56"</td></tr><tr><td>S5, S7</td><td>"57"</td></tr><tr><td>S5, S8</td><td>"58"</td></tr><tr><td>S6, S7</td><td>"67"</td></tr><tr><td>S6, S8</td><td>"68"</td></tr><tr><td>S7, S8</td><td>"78"</td></tr></tbody></table>

# 3. Methodology

In this paper, the method used for OC fault detection in the semiconductors of CHBI is based on the measurements of the output voltage and the voltage across each H-bridge of the CHBI. These voltage measurements are fed into a neural network as input and used for training to detect the failures. The neural network is composed of an LSTM layer, 1-CNN layers (which will be explained below), and fully connected layers.

## 3.1. 1D-CNN

One-dimensional neural network gained popularity in the mid-2000s, specifically in the field of computer vision, and they were used for the first time for speech recognition. 1D-CNNs are designed to handle one dimensional data, such as time series data, sequences (or signals). Kernels (filters) are used to extract features from raw data [26]. These filters have 1 dimension with K learnable elements and are slid through the whole sequence to
detect patterns, and this operation is called convolution and is defined with the equation shown below [27].

$$y_i = (x * v)[i] = \sum_{k=0}^{K-1} x[i + k] v[k] \qquad (2)$$

where:

* x is the input sequence;

* v is the filter;

* K is the size of the filter.

After each convolution, an activation function is applied, usually a ReLU. This introduces non-linearity, allowing the network to learn complex representations. The ReLU is defined as:

$$a_i = \text{ReLU}(y_i) = \max(0, y_i) \qquad (3)$$

The values of the elements of the filter are what gets updated through the learning process first comes the forward propagation where the output of the whole network is calculated then the loss function is calculated by comparing the output of the network with the desired value (target), the gradient of the loss function is calculated with respect to each learnable parameter (weights), for each weight:

$$w_{\text{new}} = w_{\text{old}} - \eta \frac{\partial L}{\partial w} \qquad (4)$$

where:

* w is the learnable parameter;

* η is the learning rate between 0 and 1;

* ∂L/∂w is the derivative of the loss with respect to that weight.

Figure 4 shows a kernel sliding a 1D sequence, performing a dot product at each position, the results of these operations are transformed into a feature map, capturing the patterns in data. The convolution is usually followed by a pooling layer, the role of this layer is to reduce and down sample the feature map by focusing on the most important features, every 1D-CNN have a fully connected layer which gives the predictions (classification or regression).

## 3.2. LSTM

To capture the temporal dependencies in the voltage signals (the voltage at a given time depends on previous states) of the H-bridges, LSTM networks are employed. LSTM networks are a variation of the recurrent neural networks, which consist of recurrent cells whose states are affected by both past states and current inputs. LSTMs are a better version of RNNs as they can handle long-term dependencies. In other words, LSTMs are better compared to traditional RNNs at handling the vanishing gradient or exploding gradient problem [28].
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Feature extraction using 1D kernels.</figcaption></figure>

* **Vanishing Gradient:** When training a model over time, the gradients (which help the model learn) can shrink as they pass through many steps. This makes it hard for the model to learn long-term patterns since earlier information becomes almost irrelevant.

* **Exploding Gradient:** Sometimes, gradients can grow too large, causing instability. This makes it difficult for the model to learn properly, as the updates to the model become erratic and unpredictable.

Figure 5 shows the structure of an LSTM unit which is controlled by three gates: **Forget Gate:** The forget gate decides which information from the previous cell state is no longer relevant and should be discarded. It takes the current input xt and the previous hidden state ht1, passes them through a sigmoid function, and outputs values between 0 and 1. A value close to 0 means the information is forgotten, while a value close to 1 means it is retained. **Input Gate:** The input gate adds useful information to the cell state. It takes the same inputs as the forget gate, and passes them through a sigmoid function and a tanh function, and then both outputs are multiplied to obtain useful information. **Output gate:** The output gate extracts the useful information from the current cell state by applying a tanh function. Then, the information is regulated using the sigmoid function on the values to be remembered using inputs $h_{t-1}$ and $x_t$. At last, the values of the vector and the regulated values are multiplied to be sent as an output and input to the next cell.
<figure><img src="image_6.png" /><figcaption>**Figure 5.** LSTM unit.</figcaption></figure>

### 3.3. *Data processing*

The voltage measurements used for classification undergo two preprocessing steps: normalization and segmentation. During normalization, the mean is subtracted from each signal, and the result is divided by its standard deviation. This ensures that all the signals used as features (namely the output voltage, the H-bridge 1 voltage, and the H-bridge 2 voltage) have the same scale. In the segmentation step, the long-series signals are divided into shorter, fixed-size windows before being fed into the network. Each window is assigned a label corresponding to the fault class. Segmentation is defined by two parameters: the window size (i.e., the number of samples per window) and the stride, which determines the distance by which the window shifts at each step. For example, with a window size of 100 samples and a stride of 50, the first window would span samples 1 to 100, and the second would span samples 51 to 150. Various combinations of window size and stride were tested in this work to identify the configuration best suited to the model. Each window is labelled according to the dominant fault type present in that segment. If multiple faults occur within a single window, the label corresponds to the most prominent fault type.

### 3.4. *Network architecture*

The complete neural network architecture used for fault detection is illustrated in Figure 6. It consists of a convolutional layer, followed by a dropout layer with a probability, and a max pooling layer. This is followed by an LSTM layer and another dropout layer with a probability. Finally, the network includes fully connected layers, respectively, and ends with an output layer.

## 4. Simulation and results

### 4.1. *Data generation and analysis*

To assess the effectiveness of the proposed detection method, a 5-level Cascaded H-Bridge Inverter (CHBI) was modeled in MATLAB/Simulink using Simscape Specialized Power Systems. The inverter operates with an input voltage $V_{dc}$ = 400 V, supplying an RL load composed of a 4 Ω resistor and a 6.5 mH inductor. A level-shifted PWM control strategy was employed to drive the inverter switches, with a switching frequency of 3 kHz, a modulation index of 0.85, and a reference signal frequency of 50 Hz.
<figure><img src="image_7.png" /><figcaption>Figure 6. Proposed network architecture for fault detection.</figcaption></figure>

Open-circuit faults were simulated by setting the gate signal of each switch to zero individually. The resulting output voltage waveforms were recorded for each scenario, producing 9,601 samples per case. These waveforms were labelled and exported to create a training dataset, as illustrated in Figure 7. Data augmentation was applied by repeating signal segments capturing the transition from healthy to faulty states. These augmented signals were then divided into overlapping segments of 300 samples, with each segment overlapping the previous one by 50 samples. Each segment was assigned a single fault label. Figure 8 illustrates the complete Simulink model used in this study. It includes: (i) the 5-level CHBI inverter model, (ii) a subsystem generating the gate signals using level-shifted PWM, (iii) a fault injection block that forces selected switches into open-circuit states, and (iv) a measurement block to capture the output signals for analysis.

## 4.2. Model training

The network was trained using the entire dataset, while validation was performed on a newly generated set of input sequences. These sequences were obtained by modifying the segmentation strategy, specifically, by changing the overlap between segments to produce new samples that the network had not encountered during training. The architecture of the network was optimized based on both accuracy and training time. Key parameters such as the number of convolutional layers, kernel size, dropout rates, the number of LSTM units, and the size and number of fully connected layers were adjusted iteratively to improve performance and convergence. The training progress is illustrated in Figure 9. The network achieved an accuracy of 99%, with the loss converging to near zero.

<figure><img src="image_8.png" /><figcaption>Figure 7. Signal labeling.</figcaption></figure>
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Figure 8. Simulink model: (a) Model. (b) Block diagramme.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Simulink model: (c) PWM Signal generation (d) OC fault generation block. (partial view; only half shown due to space, used for H-Bridge 1</figcaption></figure>
<table><caption>**Table 3.** Details of the neural network architecture used for fault diagnosis.</caption><thead><tr><td>Layer Type</td><td>Parameters</td></tr></thead><tbody><tr><td>Sequence Input Layer</td><td>Features = 3, MinLength = 300</td></tr><tr><td>1D Convolutional Layer</td><td>Filter size = 3, Number of filters = 50, Padding = causal</td></tr><tr><td>Layer Normalization</td><td>–</td></tr><tr><td>ReLU Activation</td><td>–</td></tr><tr><td>1D Max Pooling Layer</td><td>Pool size = 10, Stride = 5</td></tr><tr><td>Dropout Layer</td><td>Dropout rate = 0.5</td></tr><tr><td>LSTM Layer</td><td>Units = 60, OutputMode = 'last'</td></tr><tr><td>Dropout Layer</td><td>Dropout rate = 0.2</td></tr><tr><td>Fully Connected Layer</td><td>Output size = 200</td></tr><tr><td>Fully Connected Layer</td><td>Output size = 38</td></tr><tr><td>Softmax Layer</td><td>–</td></tr><tr><td>Classification Layer</td><td>–</td></tr></tbody></table>

<figure><img src="image_12.png" /><figcaption>**Figure 9.** Training process: (a) Accuracy. (b) Loss function.</figcaption></figure>

## 4.3. *Model testing*

To evaluate the model's effectiveness and generalization capability, several test datasets were generated by varying key parameters, including the input voltage, load conditions, switching frequency, modulation index, and measurement noise. And to be able to judge on the effectiveness of the model, the F1-score and the accuracy are used as evaluation indices. The accuracy is simple to define it's the number of correct predictions divided by the total number of predictions, the F1-score is a bit more complex where two things need to be calculated the precision and the recall, Precision is the ratio of correctly classified positive cases to the total number of cases classified as positive; Recall represents the ratio of correctly classified positive cases to the actual number of positive cases and F1-score is a combination of both which is shown in the equation below:
$$ Pr = \frac{TP}{FP + TP} \qquad (5) $$

$$ Recall = \frac{TP}{FN + TP} \qquad (6) $$

$$ F_1\text{-Score} = 2 \times \frac{\text{Pr} \times \text{Recall}}{\text{Pr} + \text{Recall}} \qquad (7) $$

Where TN represents the number of true negatives, FN the false negatives, TP the true positives, and FP the false positives. The confusion matrices for all cases are illustrated in Figure 10, where blue cells indicate correct classifications and orange cells indicate misclassifications, and the accuracy and F1-score are summarized in Table 4.

<table><caption>**Table 4.** Robustness analysis under different operating conditions.</caption><thead><tr><td>Changed Parameter</td><td>Accuracy</td><td>F1-Score</td></tr></thead><tbody><tr><td>No change</td><td>99.95%</td><td>0.9995</td></tr><tr><td>Input Voltage: ±20 V</td><td>99.95%</td><td>0.9995</td></tr><tr><td>Load: R = 1 Ω, L = 1 mH</td><td>99.77%</td><td>0.9972</td></tr><tr><td>Switching Frequency: 3 kHz → 1 kHz</td><td>92.24%</td><td>0.9107</td></tr><tr><td>Modulation Index: ±0.1</td><td>97.42%</td><td>0.9746</td></tr><tr><td>Measurement Noise</td><td>99.82%</td><td>0.9985</td></tr></tbody></table>

As shown, the accuracy and F1-score remain consistently high (above 97 %) across all scenarios, except when the switching frequency is reduced from 3 kHz to 1 kHz. In this case, both metrics drop to 92% and 0.91, respectively. This decline is due to the significant impact of switching frequency on the waveform patterns, which makes it more difficult for the network to distinguish between certain fault classes, particularly those with similar voltage signatures, as observed in the confusion matrix. Nevertheless, the performance remains within an acceptable range.
<figure><img src="image_13.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>(c)</figcaption></figure>

<caption>**Figure 10.** Training process (part 1): (a) No change. (b) Input voltage changed. (c) Load changed.</caption>
<figure><img src="image_16.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>(e)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(f)</figcaption></figure>

**Figure 10.** Training process (part 2, continued): (d) Switching frequency changed. (e) The modulation index changed. (f) Measurement Noise added.
<figure><img src="image_19.png" /><figcaption>**Figure 11.** Open-circuit fault detection in real-time: (a) Input sequences. (b) Fault label.</figcaption></figure>

## 4.4. Integration of the model in Simulink for online fault detection

To evaluate the network's online detection performance in simulation, the trained neural network was integrated into the Simulink model. Noise was added to the diagnostic signal to simulate more realistic conditions. Figure 11 shows the network's output in case of a fault occurring in T1. The fault was introduced at 0.2 seconds. It can be observed that the fault is detected in approximately 17 ms, which corresponds to the time required to capture a data window and process it through the neural network. The model achieved high accuracy in detecting open-circuit faults, which shows that combining CNN and LSTM is effective in capturing both spatial and temporal features. One strength of this approach is its robustness, even under noisy conditions, which suggests good potential for real-time applications. The method has reached an accuracy of 99.95 %.
## 5. Conclusion

This work proposes a method for detecting and locating single and double open-circuit switch failures in a 5-level cascaded H-bridge inverter (CHBI). This approach relies solely on voltage measurements, specifically the output voltage and the voltages across each H-bridge, making it simpler and more cost-effective than the existing techniques. These signals are preprocessed and are used to train a deep learning model combining a one-dimensional convolutional neural network (1D-CNN) for spatial feature extraction and a long-short-term memory (LSTM) for capturing temporal dependencies. This hybrid design allows for precise identification of single and double open-circuit faults, as well as their locations within the CHBI topology. The data set was generated through various simulations of the CHBI in Simulink. Normal operation and multiple fault scenarios (single and double) were generated by introducing switch faults at different locations and instants in the inverter. The corresponding voltage measurements were recorded and labeled, providing waveforms that can be used as inputs to the neural network. The proposed method reached high classification accuracy (>99%) and demonstrated robustness to measurement noise and variations in key system parameters, such as input voltage, load, switching frequency, and modulation index. These results demonstrate the potential of this method for reliably detecting faults in CHBIs across a wide range of operating scenarios, including variations in DC sources, modulation indices, noise levels, and load parameters. Compared to existing OC fault detection techniques used in CHBI, the key contributions of this work are the following.

* Reliance on a reduced number of measurements (voltage only);

* accurate detection of both single and double switch failures;

* high fault classification accuracy;

* robust performance under non-ideal and varied conditions.

This work presents a structured framework for detecting failures, despite the fact that the neural network is trained iteratively. The current limitation is the absence of hardware validation. Future work will focus on experimental implementation using a real inverter setup to confirm the method's practical applicability and address challenges related to real-time operation.

**Author Contributions:** Conceptualization, CH.D, K.T, and F.V; methodology, CH.D, K.T and F.V; software, CH.D; validation, K.T. and F.V; analysis, CH.D, K.T. and F.V; investigation, CH.D and K.T.; writing—original draft preparation, CH.D, K.T. and F.V; writing—review and editing, CH.D, K.T. and F.V; supervision, K.T. and F.V; project administration, K.T. and F.V.

All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author(s).

**Conflicts of Interest:** The authors declare no conflicts of interest.

## Abbreviations

The following abbreviations are used in this manuscript:
<table><tr><td>1D-CNN</td><td>One-Dimensional Convolutional Neural Network</td></tr><tr><td>CHBI</td><td>Cascaded H-Bridge Inverter</td></tr><tr><td>DOAJ</td><td>Directory of Open Access Journals</td></tr><tr><td>LD</td><td>Linear Dichroism</td></tr><tr><td>LSTM</td><td>Long Short-Term Memory</td></tr><tr><td>MDPI</td><td>Multidisciplinary Digital Publishing Institute</td></tr><tr><td>OC</td><td>Open-Circuit</td></tr><tr><td>OCF</td><td>Open-Circuit Fault</td></tr><tr><td>PWM</td><td>Pulse Width Modulation</td></tr><tr><td>SC</td><td>Short-Circuit</td></tr><tr><td>SCF</td><td>Short-Circuit Fault</td></tr><tr><td>TLA</td><td>Three Letter Acronym</td></tr></table>

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