1. Introduction
Induction motors (IMs) are well-known for their reliability as industrial drives. However, during operation, IMs are frequently subjected to hostile conditions, resulting in early deterioration or complete failure [
1]. The faults of induction machines cause excessive downtime which generates large losses in terms of maintenance and lost revenues. Even a small fault can cause important losses, such as reducing efficiency and increasing temperature, which reduce the insulation lifetime, increase vibration, and reduce the bearing lifetime [
2,
3]. All earlier cited consequences are commonly due to the operating environment conditions and internal factors of the machines that can be summarized into three main categories, which are mechanical, electromagnetic, and thermal, causing the bar damages. In addition, Ayhala et al. [
4] reported that the main failure cause is bending stress due to the electromagnetic forces generated by the action of slot linkage flux. The squirrel cage induction motors suffer from a variety of mechanical and electrical faults, of which about 10% are broken rotor bar (BRB) faults [
5]. BRB faults are one of the most common motor problems [
1]. A BRB fault can be caused by either an imperfection in the manufacturing process or turbulent operating conditions, such as direct-on-line starting duty cycles, high thermal stresses, excessive pressures, high currents that occur in the motor cage, and pulsating mechanical loads [
6]. Any asymmetry in the rotor of a squirrel cage induction motor presents unevenly distributed rotor currents. When a BRB occurs in one rotor bar, the current flowing through it is shifted to the other rotor bars, putting the adjacent bars under higher stress and potentially leading to more BBFs until the motor fails completely. The reactions of these currents to the air-gap field generate fault-specific signatures in the spectrum of the current, power, torque, and speed. For this reason, many researchers resorted to using signal processing approaches to extract the information through signal analysis techniques for the diagnosis operation, such as current, stator voltage, power, speed, temperature, and vibration signals [
7,
8,
9]. For instance, motor current signature analysis (MCSA) is a widely used technique thanks to its low cost and noninvasive nature [
10]. In the MCSA technique, the current signal of a running motor is collected and recorded. Then, the fault features are extracted from recorded data in the time domain, frequency domain, or time–frequency domain using signal processing techniques. Among the most commonly used signal processing tools for IM fault diagnosis in the literature are: fast Fourier transform (FFT), short-time Fourier transform (STFT), wavelet transform (WT) [
9,
11], and empirical mode decomposition (EMD) [
12]. Generally speaking, FFT is an appropriate technique for BRB fault detection in the steady state [
11,
13]. However, the application of this technique has some limitations, which especially affect the diagnosis of BRB faults at low load or no load (low slip). From these limitations, the characteristics of the sideband components, (1 ± 2
ks)
fs (
s is the rotor slip,
fs is the fundamental frequency, and
k = 1, 2, 3 …), are very close to the fundamental frequency component [
9], and the normal spectral leakage can obscure frequency components characteristic of the fault [
14]. High-resolution techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) and multiple signal classification (MUSIC), as presented in [
15,
16,
17], have recently sparked a lot of attention. However, the precision of the utilized sensor has a major impact on those techniques, and a significant computing burden is necessary. Therefore, some development and adaptation of low-resolution BRB diagnosis techniques are preferred to using such high-resolution techniques.
The envelope of Hilbert transform (HT) with FFT (HFFT) is proposed in this paper to adopt the FFT technique. HT is used to obtain the envelope from the stator current in the transient regime and at the steady state of IM. Then, it is processed by the FFT. The envelope signal occupies the low-frequency spectral region, and its analysis offers better detection than that of the spectrum of the original signal, as the power frequency is eliminated from the signal [
18]. This method gives good detection of the BRB fault, especially at low slip. Within the same framework, many researchers have focused on artificial intelligence (AI) techniques as powerful tools for motor fault classification and autodetection [
9,
19,
20,
21,
22,
23,
24,
25]. These tools do not require accurate modeling of the system, and they provide high efficiency in autodiagnosis. The vector features are extracted from fault detection techniques, and they are then used as inputs for AI tools such as fuzzy logic, neural networks, or combinations (neuro-fuzzy networks) for fault recognition [
26]. The combination of diagnostic techniques and AI tools has permitted great developments in the field of the monitoring and maintenance of industrial machines and processes with optimal effort and an effective cost.
Some recent works involve BRB detection without a classifier in Refs. [
27,
28]. One can see successive variable mode decomposition (SVMD) for BRB detection in IMs. Based on signal energy, the stator starting current is used. Then, a quadratic regression curve method is utilized in order to achieve the detection objective. A cyclic modulation spectral analysis of vibratory signals was proposed by Zuolu Wang et al. [
28], and the obtained results show the efficiency of the proposed method for BRB fault identification. However, regarding the nature of the signal used, the earlier detection of faults is questionable since mechanical signals are less sensitive compared to electrical signals. However, a considerable amount of literature has examined the diagnosis and classification of BRB faults in IMs. In [
29], the authors presented an experimental diagnosis study about BRB faults in IMs based on the HFFT method of the stator current using a fuzzy system. They used two features extracted from the HFFT method, namely, the amplitude of the 2
sfs harmonic and the DC value. These features were used as the inputs of the fuzzy logic system for the decision making about the rotor state. Gyftakis et al. [
30] proposed an innovative diagnostic method based on Park’s vector approach to detect BRB faults in IMs. The method consists of the monitoring of the higher harmonic index after applying elliptical and notch filters on the Park’s vector components of the stator currents. The filtered Park’s vector modulus is calculated and processed by the FFT to detect the fault by giving the amplitudes of the 2
ksfs signatures. It is noted that this method and the HFFT approach give the same spectrum of the BRB fault, which is equal to 2
skfs. However, the filtered Park’s vector method needs three current sensors to be carried out, whereas the HFFT method needs only one sensor. Furthermore, in Ref. [
11] one can see a hybrid diagnosis approach based on Hilbert and discrete wavelet transform (HDWT) for BRB faults in IMs. The proposed method used the HT to obtain a stator current envelope to be processed via DWT. This work has a significant result, but the WT has some drawbacks, including the arbitrary selection of the mother wavelet, which may introduce an inappropriate fault detection [
18]. Harzelli et al., in [
9], presented a method for the diagnosis of simple and mixed BRBs and stator short-circuit faults in the closed-loop drive for IMs. The authors adopted two strategies: The first was based on the model used to generate a residual speed signal to indicate the presence of possible failures using the high-gain observer in the closed-loop drive. The second was based on HFFT. The amplitude and frequency of the harmonic 2
sfs extracted from the HFFT were used as fault indicators and were considered as inputs for the neural network (NN) in order to identify serval possible faults and distinguish between them. In the work conducted by [
19], the authors applied the support vector machine (SVM) and an NN to diagnose BRBs and to show faults in IMs based on the vibration and the instantaneous power signals of IMs. In both cases, the dimensionality of the signals was reduced using principal component analysis (PCA), and the selected features were then sorted in order of importance using the sequential floating forward selection (SFFS) approach to reduce the number of input features and discover the most ideal feature set. The chosen features were then classified by the SVM and ANN methods. The obtained results revealed that ANN performs better than SVM. Despite the good technical aspects of the proposed approach, it remains costly because of its calculation complexity. Furthermore, in Ref [
22] the researchers proposed a simulation-based study in which a combination of the HFFT and an NN was proposed to detect and quantify the number of broken bars in the rotors of IMs under various load conditions. The amplitudes and frequencies of the 2
sfs harmonic were extracted from HFFT as fault indicators to build the NN for autodiagnosis purposes. Despite the effectiveness of this approach, it is still insufficient to confirm its applicability. For that, an experimental test should be carried out. One can also find other BRB detection approaches, such as the inverse thresholding to spectrogram method presented in Ref. [
31]. A simulation performed under ANSYS Maxwell 2D led to efficient BRB detection. An interpolated kernel density estimate for IM diagnosis was proposed in Ref. [
32], where the fault signature was extracted from the vibration signal. In Ref. [
33], a DWT + fuzzy incipient BRB detection was proposed. The method provides higher efficiency, but the optimization of fuzzy MFs and the selection of appropriate IF–THEN rules governing the fuzzy inference system is a complex task. In Ref. [
34], a CWT + PCA+ ANFIS diagnostic algorithm was proposed. The simulation results showed that the algorithm performs well in the presence of PCA. However, based on the nature of signal processing techniques, it is well-known that continuous wavelet transform requires substantial time and CPU capacity compared to the discrete technique. In addition, the use of five parallel ANFISs increases the computational complexity of the detection algorithm compared to the other techniques. In Ref. [
35], the authors proposed an ANFIS classifier based on stator current Id and Iq under different loads. Although the obtained results seem efficient, their validation in practice is still debatable since temporal stator current signals do not provide explicit information about motor reliability. In Ref. [
36], Merabet et al. proposed a multimesh model based on a BRB detection approach of three phases of IMs. the signal processing technique used was the wavelet packet method. The use of the ANFIS classifier proves its effectiveness. However, experimental validation is still necessary in order to evaluate the consistency of the proposed WP + ANFIS technique. Dias et al. [
37] proposed an experimental FFT + ANFIS BRB diagnosis of IMs. The main important experimental contribution was the use of three faulty scenarios, which were one BB, two adjacent BRBs, and two nonadjacent BRBs. Regarding the ANFIS classifier, a grid partitioning identification method was used in order to build the initial fuzzy inference system (FIS). The obtained results show the efficiency of the proposed method, even at very low slip. However, the main drawbacks of that method are the number of modifiable parameters when the number of inputs is ≥6 and that selected MFs are important, which requires high computational performance in order to perform the training phase. In Ref. [
38], the authors proposed an FFT + fuzzy BRB detection of squirrel cage induction motors. The advantage of this method is that the developed multiwinding model of IMs includes four BRBs and correctly detects the nature of faults based on the FFT of the current signals, which allows the extraction of amplitude (1 ± 2
s)
fs harmonics. However, a lack of experimental validation and the nature of the classifier, which requires trial-and-error tuning based on human expertise, are the most relevant drawbacks. In a major advance in BRB detection algorithms, Mikko Tahkola et al. [
39] developed an ATSC- NEX algorithm. The results show the effectiveness of this automated machine learning algorithm in correctly evaluating the state of IM rotor bars. The main advantage of ATSC- NEX is the autoconstruction of the model and its ability to overcome the overfitting phenomenon thanks to the introduced early stopping criterion. However, the optimization of the hyperparameters is the main disadvantage of the algorithm. Even if nested cross-validation provides good results at present, the optimization is still computationally expensive.
Generally, the summary of a fault diagnosis planner is data (or signal) acquisition, feature extraction, and classification [
40]. Relevant to the aforementioned studies, the present work introduces a new approach in which the HFFT method is applied to the stator current with the ANFIS to detect and identify the number of broken bars in the rotor under different conditions. The proposed approach offers the advantage of providing a data-driven diagnostic model that can attain the objectives in question without the necessity of a complex mathematical model. The ANFIS is a specific type of neuro-fuzzy classifier approach integrating the NN adaptive capability and the fuzzy logic qualitative approach. It has been successfully applied for automated fault detection and diagnosis in IMs [
41].
The main contributions of this study are as follows:
A new combined HFFT-ANFIS is proposed as an effective real-time diagnosis method to detect BRB faults in IMs.
Two ANFIS models, namely, grid partitioning (GP) and subtractive clustering (SC), are suggested and validated through experiments for the detection of BRB faults.
The proposed HFFT-ANFIS-GP and HFFT-ANFIS-SC models were carried out and validated based on experimental results aiming at detecting and quantifying the broken bar numbers under different conditions. The HFFT approach was applied through HT, which is used to extract the stator current envelope and process it by FFT. The frequency and the amplitude of the 2sfs harmonic extracted from the HFFT were used as BRB fault indicators and considered as the ANFIS input for autodetection.
This paper is structured as follows: In
Section 2, a description of the HFFT technique and the adaptive neuro-fuzzy system are presented. In
Section 3, the methodology of fault detection is proposed. A description of the test bench and the experimental test is presented in
Section 4. In
Section 5, the experimental results and discussions are introduced, and the conclusion is presented in
Section 6.