A new ensemble-based classifier for IGBT open-circuit fault diagnosis in three-phase PWM converter

Nowadays, induction motor drive systems fed by three-phase pulse width modulation (PWM) converters have been widely used in industrial applications [1]. With the advance of power semiconductor technology, insulated gate bipolar transistors (IGBTs) are commonly used in such systems to adjust the output signal of the converters. However, according to the industrial statistics, 38% of the converter faults are caused by the failure of power device [2] [3]. Power devices faults in converter may result in unstable output voltage and frequency, and can lead to the shutdown of the drive system. Thus, fast and accurate fault diagnosis method for IGBT has attracted extensive attentions [4].

In general, IGBT faults can be categorized into open-circuit fault and short-circuit fault. Short-circuit fault is usually caused by over-voltage or overheating. In practice, short-circuit fault of IGBT is usually protected by standard protection system, such as fuse and disconnecting switch [5].As a result, the abnormal state caused by short-circuit, such as over-current, may only last a very short period. On the other hand, open-circuit fault usually results in sustained period of abnormal states and can significantly degrade the converter performance. For PWM converters, the open-circuit situation is more complex because of the existence of a number of IGBTs in a converter. When open-circuit fault occurs, it is necessary to quickly detect and locate the faulty IGBTs. Hence, this paper concentrates on IGBT open-circuit faults in three-phase PWM converters.

Diagnosis methods of IGBT faults can be categorized generally into model-based, signal-based, knowledge-based, hybrid and active methods [5]. For model-based method [6–11], models of industrial processes or systems are the foundation and have to be derived from physical principles or systems identification techniques. The measured outputs of system models are then compared with the predicted outputs and the consistency is evaluated to diagnose faults [5]. This requires a high-level understanding of the practical systems and the consideration of other environmental factors in the actual working situation. Therefore, model-based method always requires tiresome and length tuning for an accurate. For signal-based methods [12–16], measured signals are used for the diagnostic process. Feature selection, such as RELIEFF [17], is a methodology to evaluate the quality of signal features according to their distinction among instance near each other and to select several top best features. Similarly, feature extraction is also an approach to decrease the signal dimension, such as the principle component analysis (PCA) [18]. This is achieved by transforming original datasets into a reduced set of features. The initial features are selected as a subset, which contains the relevant information from the input data. By those features, diagnostic algorithm can analyze symptoms to make a fault diagnosis decision. However, such signal-based methods require long processing time and the diagnostic performance is easily affected by fluctuation of loads.

Both model-based and signal-based methods require a prior knowledge on system models or signal patterns. Furthermore, signal-based method is easily influenced by load fluctuation, and this is a significant drawback in online application. Hence these methods are either sensitive to system load or with low detection speed. On the contrary, knowledge-based method [19–25] is based on large volumes of historical data [26] which can be obtained by simulations and experiments. The artificial intelligent technique can also be combined with data-driven methodology to extract the mapping relationship knowledge between the online input data and the diagnosis results according to the historical data. Thus, this method is also called a data-driven method.

In order to improve fault diagnosis, this paper develops a data-driven fault diagnosis method for PWM converter fed induction motor drive system. The inputs in the fault diagnosis scheme are three-phase currents and the outputs are fault types and location. Because load current is measured for the control algorithm of PWM converters, no additional sensors are required in the system. A novel learning network, namely extreme learning machine (ELM) [27] is applied to develop a diagnostic method for IGBT open-circuit faults. ELM is a randomized learning neural network, whose input weights and bias are randomly determined in ELM learning and the output weights are computed without traditional iteration. As a result, instead of lengthy training time, ELM has a fast learning speed which allows it to solve problems with large volumes of data. Due to its fast learning speed, ELM has already been used in detection of microgrid islanding events [28] and real-time dynamic security assessment of power systems [29]. However, this method has not been adopted in fault diagnosis of power electronics devices. To guarantee accuracy, the ensemble structure is adopted which contributes to increasing the robustness of diagnosis performance. In addition to improve learning process, the relevance between the time window width of signal sampling and diagnosis accuracy is analyzed to select the suitable time window. Thus, the diagnosis performance of the scheme can be significantly improved.

Through online application, it shows that due to the fast learning speed of ELM, the proposed scheme is feasible to identify IGBT open-circuit faults with balanced diagnostic accuracy and speed. The simulation also validates that the classification performance is independent of voltage ripple, and harmonics, speed and load fluctuations.

Previous studies of ELM have focused on both regression and classification problems. ELM shows performance of high learning-speed and strong generalization capacity. However, during ELM training process, the input layer weight ω_IH, is generated randomly. Due to the stochastic value, ELM always suffers from inadequate consistency and stabilization [32]. To increase the accuracy, this paper designs an ensemble learning process.

4.2 Ensemble classifier structure

The structure of ELM ensemble classifier is depicted in Fig. 6. As shown, after the training process, the training features can be decided in individual ELM classifier, such as the output weights. Then the well trained ELM classifiers are clustered as an ensemble model.

Based on the operating data in real-time, the proposed ensemble classifier is applied online for fault diagnosis. Using the decision-making mechanism, the final diagnosis output can be selected among individual ELM classifiers [30]. In addition, unlike traditional SLFN with long training time, ELM has high learning speed, and therefore, the proposed ensemble model is efficient.

In the testing process, each single ELM has its output t as t = [t₁,…,t_J], where J is the number of output features. Assuming the total number of single ELM is p, a series of output T can be achieved as

$$ \mathbf{T}=\left[\begin{array}{c}{\mathbf{t}}_1\\ {}\vdots \\ {}{\mathbf{t}}_p\end{array}\right]=\left[\begin{array}{ccc}{t}_{11}& \cdots & {t}_{1J}\\ {}\vdots & \ddots & \vdots \\ {}{t}_{p1}& \cdots & {t}_{pJ}\end{array}\right] $$

(13)

For each column in (13), it represents the output results of each feature. The voting process is to choose the value with the most frequent occurrence in every column. Then a 1 × J output vector, where each element is the most common value of each feature, can be obtained by:

$$ {y}_j=\underset{i\in \left\{1,2,\dots, p\right\}}{\mathrm{mode}}\left\{{t}_{ij}\right\} $$

(14)

where y_j is the final result of the j^th instance, “mode” is the mathematical function for finding the mode of this sub-output set {t_ij}. Unlike errors existing in single ELM classification, this ensemble scheme minimizes the error as much as possible and the classification output is credible and reliable.

4.3 Diagnostic time window selection

In the training process described above, every input data can be expressed as x = [x₁,…,x_D] with D features, where D is also called dimension. With higher dimension, diagnosis can achieve a higher accuracy with more learning time.

In the process of sampling, an exact length of waveform in time domain, namely time window, is selected. When the window width is not enough, information of fault signal cannot be fully achieved, leading to inadequate learning process, and resulting in errors in diagnostic system. On the other hand, when the window width is too large, although diagnostic accuracy is able to be guaranteed, the burden of learning process is increased and efficiency is low due to the lengthy learning time. Considering of applying to real-time online process, to keep balance between the cost of time and diagnostic accuracy is important. Therefore, to select appropriate time window width for fault diagnosis is an important task in testing process. In this study, diagnostic time window selection is equivalent to finding the appropriate sampling length of training data.

Fig. 7 illustrates a part of sampled waveform. In this figure, the horizontal axis represents the number of sampling point, which is also the time window width in this study. As seen, when the sampling number is 100, the sampled waveform is less than 1/4 periods, and does not provide reliable data for diagnosis. However, when the time window width reaches 400 or 500, the sampled waveform forms a period, and contains adequate information of the three-phase currents. On the other hand, if the sampling window width increases further, the information will be redundant and under this circumstance, the learning process will consist of lengthy calculation with reduced efficiency. By adjusting the sampling length, the diagnostic performance will achieve a trade-off between accuracy and learning burden.

To verify the proposed data-driven based fault diagnosis method, a comprehensive and informative database is the fundamental requirement. In order to generate the database, a three-phase PWM voltage source inverter based induction motor drive system is simulated.

5.2 Parameter selection

Given different activation function searching patterns and neuron nodes, the performance comparison of ELM settings is analyzed in order to select optimal training parameters. To decide the relationship between single learner parameters and accuracy, the test accuracy A can be defined as:

$$ A=\frac{\left(N-M\right)}{N}\times 100\% $$

(15)

where N is the total number of instances in test dataset, M is the number of misclassified datasets. To seek the optimal parameters, five types of activation function, i.e. triangular basis (Tribas), radial basis (Radbas), sign function (Sig), sine (Sin), and hard-limit (Hardlim) are compared in terms of optimal classification performance, in order to decide the optimal hidden node range for single classifier. The classification performance is shown in Fig. 8:

Fig. 8 shows that the sign activation function outperforms than the other four initial activation functions. Meanwhile, the trends of the curves indicate that increase of neuron nodes gradually increases the accuracy and in certain range, the accuracy is stable. As seen from Fig. 8, in the range from 1500 to 2000 using sign function, the accuracy of individual ELM classifier can be assured.

5.3 Time window selection

In this study, the simulation data is a 6600 × 1800 matrix, where 6600 refers to the number of datasets and 1800 indicates the number of data features in the time domain fault data. Every 600 features indicate the current data of each phase. For 10 kHz sampling frequency used in the simulation, every 100 data features correspond to a time window of 10 ms. Hence, the maximum time window in this study is 60 ms. When the length of sampling signal (i.e. time window width) varies from 10 ms to 60 ms at the interval of 10 ms, the classification performance is summarized in Fig. 9.

According to Fig. 9, increase the window width increases the testing accuracy. When the window width is short, e.g. 10 ms, classification accuracy is relative low due to inadequate learning. Meanwhile, when the time window width reaches a certain value, the accuracy stays high and becomes stable. Thus, in this case, 40 ms is determined as the optimal time window width, which guarantees the high accuracy with acceptable learning time.

This paper designs an ensemble-based randomized classifier to identify IGBT open-circuit faults in three-phase PWM converters. Considering both single and double IGBT faults, the output three-phase converter currents are measured using the simulation model, and faults are diagnosed by ELM. To compensate the inadequacy of single ELM classification, an ensemble structure is designed to increase accuracy by combining a number of single classifiers. Moreover, an optimal value of time window is adopted to balance the tradeoff between diagnostic accuracy and ELM learning burden. The simulation shows that, compared with other traditional learning algorithms, ELM has better performance in both classification accuracy and learning time. This ensemble ELM structure can identify IGBT open-circuit faults with a much higher diagnosis accuracy of 96.89% in 40 ms. It also shows that the proposed data-driven scheme is independent of voltage ripple, harmonics, and speed and load fluctuations. Thus, the proposed scheme is efficient and reliable in practical applications.