Diagnosis of an Inverter IGBT Open-circuit Fault by Hilbert-Huang Transform Application

Diagnosis of an Inverter IGBT Open-circuit Fault by Hilbert-Huang Transform Application

Bilal Djamal Eddine Cherif* Azeddine Bendiabdellah Mostefa Tabbakh 

Diagnosis Group, Laboratory LDEE, Electrical Engineering Faculty, University of Sciences and Technology of Oran, Oran 31000, Algeria

Department of Electrical Engineering, University of M’sila, M’sila 28000, Algeria

Corresponding Author Email: 
cherif.doc84@gmail.com
Page: 
127-132
|
DOI: 
https://doi.org/10.18280/ts.360201
Received: 
12 January 2019
|
Revised: 
20 March 2019
|
Accepted: 
29 March 2019
|
Available online: 
5 July 2019
| Citation

© 2019 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

The open-circuit fault of an inverter IGBT switch leads to total or partial loss of control of the phase currents resulting in the dysfunction of the system. Moreover, if the fault is not detected and compensated quickly, it can cause complete shutdown of the system. To ensure the system service continuity, efficient and fast techniques for detecting and locating the open-circuit fault of the IGBT must be implemented. This paper proposes a Hilbert-Huang Transform (HHT) based on the detection of the IGBT open-circuit fault. The proposed technique is based on the complete empirical mode decomposition with adaptive noise (CEEMDAN). This mode is applied to the motor stator current signals to obtain a function called the intrinsic mode function (IMF). The IMF contains the frequency (and its multiples) related to the frequency of the harmonic characterizing the IGBT switch open-circuit fault of the inverter. In order to test the effectiveness of the proposed technique and validate the results, several experimental tests are performed using a test bench.

Keywords: 

inverter, IGBT, open-circuit, HHT, EMD, CEEMDAN, IMF, spectral envelope, RMS

1. Introduction

In general, maintenance is intended to ensure the maximum availability of production equipment at an optimal cost under good conditions of quality and safety. The general principle of the diagnostic algorithms is based on the use of the data recorded on the system and the knowledge that one possesses of its healthy operation (for the detection) or its faulty operation (for the location). These algorithms develop symptoms that reveal the faulty behavior and the nature of the dysfunction. In this framework, static converters, particularly inverters, are mainly present in variable speed electrical drive systems. Reliability data; from the literature; justify the envisaged scope for the implementation of fault tolerance or failure. Figure 1 shows the distribution of faults in% in an inverter [1].

Figure 1. Distribution of faults in % in a static converter

Among these diagnostic methods there are spectral analysis techniques based on the Fourier transform (FT). The FT provides a good description of the stationary and pseudo-stationary signals but has many limitations when the signals to be analyzed are not stationary. In this case, the solution would be to use the so-called time-frequency analysis tools. These methods include: the STFT and the Hilbert-Huang Transform (HHT) [2].

The authors Hilbert and Huang have recently proposed a technique that approaches in another angle the problematic of non-stationary signal analysis with the empirical modal decomposition (EMD) approach. The EMD adaptively decomposes a signal in a sum of oscillating components. Unlike FT or wavelets, the basis of the EMD decomposition is intrinsic to the signal. One of the motivations for the development of the EMD is the estimation of the instantaneous frequency (IF) of the signal. Indeed, the conventional approach of estimating the IF based on the Hilbert transformation (HT) is strictly limited to single-component signals. Thus, constraints are imposed on these oscillating components to correctly estimate the IF (with a physical sense) specific to each component present in the signal. The EMD combined with the HT or another method of estimating the IF results in a time-frequency representation (TFR). The EMD is defined by a process called sifting, which decomposes the signal into basic contributions called empirical modes or intrinsic mode functions (IMF). These are signals of amplitude modulation - frequency modulation type mono-component (in broad sense) each of zero average. The principle of the EMD is based on an adapted decomposition describing the signal locally as a succession of contributions of fast oscillations (high frequencies) on slower oscillations (low frequencies) [3-6].

Several papers have been published in this diagnostic field based on the HHT. The author in the paper [7] presents a method using the spectral envelope of the stator current for the online automatic detection of broken bar faults. In this paper, the HHT is used to estimate the severity of faults for different loads using classification techniques. The spectral envelope of the stator current makes it possible to read the frequency relative to the fault, which confirms the existence of the fault. The author [8] proposes a method based on the complete empirical ensemble mode decomposition with adaptive noise (CEEMDAN) associated with an optimized Thresholding operation. The CEEMDAN is first applied to the vibration signals to obtain a series of functions called the IMF functions. An approach based on the energy content of each mode with the white noise characteristic is then proposed to determine the trigger point to select the relevant modes. The author in paper [9] presents a rolling fault diagnosis method based on an improvement time-time of Hilbert (HTT a derivative of the HHT) with the main component which is the Denoising HTT transform matrix. The HTT was performed on vibration signals to deduce the transformed matrix. The main component is then used to attenuate the noise of the HHT matrix in order to improve its robustness and extract information and characteristics of the bearing fault.

This paper proposes an HHT-based diagnostic method for detecting the open-circuit fault of an IGBT in an inverter. The proposed technique is based on complete ensemble empirical mode decomposition with an Adaptive Noise (CEEMDAN). This mode is applied to the motor stator current signals to obtain a function called the intrinsic mode function (IMF) containing the frequency of the harmonic relative to the harmonic characterizing the IGBT fault. In order to test the effectiveness of the proposed technique and validate the results, several experimental tests are carried out on the system using a practical test bench at our LDEE laboratory, consisting of an induction motor powered by a two-level three-phase faulty voltage inverter controlled by the MLI-SVM strategy.

2. Hilbert-Huang Transform

In this section, the principle of HHT will be presented as well as the different versions of decomposition in empirical modes (EMD and CEEMDAN) in addition to the spectral envelope and RMS.

2.1 EMD algorithm

The EMD method decomposes the signal into a finite number of IMFs and a residue. It should satisfy the following conditions [10]:

1) The number of extrema and the number of zero-crossings are equal or differ by one.

2) The mean value of the envelopes defined by local maxima and local minima is zero.

For a given signal (t)  , the EMD algorithm is described in the following steps [10]:

1st step: Initialize: r0 = (x(t)) and i=1

2nd step: Extract the iIMF.

(a) Initialize hi(k-1) = ri, k=1.

(b) Extract the local Max and Min of hi(k-1).

(c) Interpolate the local Max and Min with cubic spleen lines to form the upper and lower envelopes of hi(k-1).

(d) Calculate the average mi(k-1) of the upper and lower envelopes of hi(k-1).

(e) Let hik = hi(k-1)- mi(k-1).

(f) If hik is an IMF, set IMFi=hik, otherwise go to step (b) with k=k+1.

3rd step: Define ri+1=ri-IMFi.

4th step: Continue the process until the final residue rn satisfies the predefined stopping criterion. The stopping condition (SD) is calculated from the two consecutive sifting results, namely hk-1 and hk as [9]:

$S D(i)=\sum_{t=0}^{T} \frac{\left|h_{j, i-1}(t)-h_{j, i}(t)\right|^{2}}{\left(h_{j, i-1}(t)\right)^{2}}$ (1)

where: T is the time duration. The sifting process is terminated when the SD value is greater than a certain threshold. Here a typical value of SD can be set between 0.2 and 0.3 [9].

The signal can be expressed as follows:

$x(t)=\sum_{i=1}^{n} c_{i}+r_{n}$ (2)

2.2 CEEMDAN algorithm

The Complete Empirical Ensemble Mode Decomposition an Adaptive Noise (CEEMDAN) is used to solve the EEMD problem related to residual noise and also to the existence of modes with different numbers. The CEEMDAN algorithm is illustrated by the following steps [11]:

1st step: Use the EMD to decompose I realizations of $x+\varepsilon_{0} \omega^{i}(i=1, \dots \dots, I)$ in order to obtain its first modes and to calculate the first mode of the CEEMDAN as follows:

$\overline{I M F}_{1}=\frac{1}{I} \sum_{i=1}^{I} E_{1}\left(x+\zeta \omega_{i}\right)$ (3)

With x, ωi: Gaussian white noise with N(0,1), ε a noise standard deviation, I: Number of sets.

2nd step: Calculate the first residue $r_{1}=x-\overline{I M F}_{1}$.

3rd step: Use the EMD to decompose $r_{1}+\varepsilon_{1} E_{1}\left(\omega^{i}\right),(i=1, \ldots \ldots, I)$ to get its first modes and define the second mode of CEEMDAN as:

$\overline{I M F}_{2}=\frac{1}{I} \sum_{i=1}^{I} E_{1}\left(r_{1}+\zeta_{1} E_{1}\left(\omega^{i}\right)\right)$ (4)

4th step: For k = 2…, k, the residue is given as follows:

$r_{k}=r_{k-1}-\overline{I M F}_{k}$ (5)

5th step: Use EMD to decompose the realizations  rkkEk(wi), (i=1,...,l) 

and define the (k+1)th CEEMDAN mode as follows:

$\overline{I M F}_{k+1}=\frac{1}{I} \sum_{i=1}^{I} E_{1}\left(r_{k}+\zeta_{k} E_{k}\left(\omega^{i}\right)\right)$ (6)

With Ek(.): kthIMF product to obtain par the EMD.

6th step: Go to step 4 for the next k.

7th step: Iterate steps 4-6 until the resulting residue can no longer be decomposed by the EMD. The final residue is given as follows:

$r_{n}=x-\sum_{i=1}^{n} \overline{I M F}_{i}$ (7)

So that the given signal can be expressed by:

$x=r_{n}+\sum_{i=1}^{n} \overline{I M F}_{i}$ (8)

With: n: The total number of modes, εk: The amplitude of the added white noise, w : White noise with the unit variance.

In this paper, the proposed technique is represented by the flowchart of Figure 2 as follows:

Figure 2. Organizational chart of the proposed method

3. Experimental Results and Interpretation

The three-phase inverter used is this work is an IGBT-based three-phase (SEMI-KRON) controlled by the DSPACE 1104 Card. The inverter IGBTs are controlled by the MLI-SVM strategy. The motor used is of a three-phase squirrel cage type; with a nominal power of 3 Kw, a frequency of 50 Hz and a nominal rotor speed of 1440 rpm.

This motor is mechanically coupled to a DC generator used as a load. The measuring system has three voltage sensors (TEKTRONIX P5200) and three Hall-Effect current sensors (FLUCK i30s (AC/DC CURRENT CLAMP)), a tachometer (ONO SOKKI HT-341) and an acquisition card (NI-6330). Finally, the whole set is connected to a computer for visualizing the processed acquired signals as shown in the photo of Figure 3 [12].

Figure 3. Photo of experimental test-rig [12]

Table 1 presents the induction motor parameters and specifications.

Table 1. Parameters of the induction motor

Rated Power

3 KW

Supply frequency

50 Hz

Rated voltage

380 V

Rated current

7A

Rotor speed

1410 rev/min

Number of rotor bars

28

Number of stator slots

36

Power factor

0.83

Number of pair of poles

2

Figure 4 shows the structure of the two-level three-phase voltage inverter. The system consists of a three-phase voltage inverter with two levels based on faulty IGBT switches supplying an induction machine.

Figure 4. Structure of the converter-motor assembly with open-circuit fault

All the acquisitions were made in nominal mode over a period of 5 seconds with a sampling frequency of 1.5 kHz.

The various modes of operation of an inverter-motor assembly made to validate the diagnostic procedure are:

  • Operation with a healthy inverter.
  • Operation with an inverter open-circuit fault of IGBT K1.
  • Operation with an inverter open-circuit fault of IGBT K2.

Figure 5 shows the stator current ias in both the healthy and open-circuit faulty cases.

Figure 5 shows the stator current in the normal and abnormal operation of the system. The stator current is characterized with respect to the normal regime by a sudden variation at the instant of the application of the open-circuit fault at the K1 switch resulting in a loss of the positive half-cycle of the current. On the other hand, in the case of a fault at the switch K2, a loss of the negative alternation is observed.

Figure 6 depicts the selected IMFs in the healthy and the open-circuit faulty cases.

Figure 5. Stator current

Figure 6. IMF

3.1 Statistical study

RMS: it is a very characteristic value of the signal, since it has a direct relation with the energy contained in it:

$R M S=\sqrt{\frac{1}{T} \int_{0}^{t} I M F^{2}(t) d t}$ (9)

where: IMF (t) is the representative function of the signal and "t" is the analysis time.

Table 2 presents the RMS value of each IMF. After analyzing the results obtained in Table 2 for each IMF we observed logic in IMF1 that identifies the IGBT fault. K1, 3, 5 are always lower than the values K2, 4, 6 respectively in the case of an open-circuit fault. The IMF1 signal is therefore the one to be used to detect and locate the harmonics that characterizes the open-circuit fault IGBT.

Table 2. RMS value of each IMF

State

IMF1

IMF2

IMF3

IMF4

IMF5

Stator current ias

Heathy

82.7486

251.5579

392.6519

93.3259

54.5970

Open K1

66.5023

188.9364

296.1187

65.0230

44.5439

Open K2

67.2751

166.4776

292.3342

66.4481

37.1695

Open K3

95.8056

299.4912

478.9089

104.0427

70.1447

Open K4

90.9676

290.8385

463.9138

103.9608

55.5179

Open K5

85.4351

251.0758

439.7275

87.3736

56.3215

Open K6

85.5871

270.7648

421.8767

96.9308

55.5287

Stator current ibs

Heathy

78.1394

224.4353

400.2280

84.5102

51.3490

Open K1

83.8776

279.4305

428.2569

98.4593

59.4270

Open K2

80.4376

259.0379

434.0585

95.5906

63.0193

Open K3

62.0850

176.8302

293.7399

64.8789

47.8038

Open K4

64.0776

180.4871

301.9739

66.5300

41.6790

Open K5

92.9372

276.8886

495.3335

116.2171

68.1189

Open K6

93.9432

282.5435

474.9952

103.5992

62.0288

Stator current ics

Heathy

80.5750

257.3659

422.6741

99.5111

60.7217

Open K1

94.2862

289.2505

485.5097

102.1549

59.3271

Open K2

91.8464

312.2062

478.6916

103.9096

69.4545

Open K3

82.6258

282.0376

428.4229

102.7446

54.2908

Open K4

79.8416

262.4210

422.2279

103.5813

56.0183

Open K5

62.3196

194.9059

297.3967

63.2087

43.7761

Open K6

65.1510

181.7017

304.8296

68.6557

42.2992

3.2 Hilbert spectral envelope

The characteristics of the Hilbert spectral envelope are quoted as follows:

(a) Elimination of the fundamental (50 Hz) of the current spectrum.

(b) Shifting of all frequency signatures to the left of 50 Hz.

(c) Visibility of the frequency signatures of the faults those are generally of very low amplitude due to the absence of the fundamental.

(d) Visibility of the frequency signatures of faults allowing the use of the linear scale instead of the semi-logarithmic scale.

(e) Elimination of the fundamental; only one characteristic frequency component of the fault appears instead of the three lateral bands multiple of 2. As for example for the signature of the open-circuit fault of an IGBT of the inverter.

Figure 7 shows the IMF spectral envelope in the healthy case and the case of open-circuit fault at the IGBT switches K1 and K2.

Figure 7. Spectral envelope

Figure 7(a), the harmonic fs is no more visible because of the Hilbert spectral envelope effect that causes the elimination of this harmonic and the shift of all frequencies to the left of the harmonic of 50 Hz. This explains the existence of the harmonics (fsh2) and (fsh4).

In the case of the open-circuit fault of the IGBT switches at K1 and K2, depict the existence of the fundamental harmonic fs (50 Hz) and other harmonics 2fs (100 Hz), 3fs (150 Hz) and 4fs (200 Hz). This explains the existence of the harmonic (fsh1), (fsh3) and (fsh5) in the Hilbert spectral envelope shown in Figure 7(b) and 7(c) hence replacing the harmonic (fs + fs), (fs + 3fs) and (fs + 5fs) with a shifting of 50 Hz.

Table 3 summarizes the amplitudes of the harmonics (fsh1, fsh2, fsh3, fsh4 and fsh5) of the spectral envelope in the healthy and the IGBT open-circuit faulty cases.

Table 3. Amplitude of the (fsh1, fsh2, fsh3, fsh4 and fsh5) of spectral envelope

Harmonics

fsh1(dB)

fsh2 (dB)

fsh3 (dB)

fsh4 (dB)

fsh5 (dB)

finst (Hz)

Healthy case

0

1.746

0

0.4605

0

0

Open K1

1.309

0

0.7544

0

0.707

50

Open K2

1.344

0

0.9951

0

0.4363

50

Open K3

0.8918

0

0.8079

0

0.4424

50

Open K4

1.329

0

0.7949

0

0.7329

50

Open K5

1.268

0

1.04

0

0.5924

50

Open K6

1.316

0

0.7719

0

0.615

50

 

According to Table 3, a comparative analysis between the healthy and the IGBT open-circuit fault cases clearly shows a frequency signature at about (50Hz) for the Hilbert spectral envelope. It should be noted that this frequency is the one that characterizes the open-circuit fault of the IGBT. In order to confirm the validity of this observation, the instantaneous Hilbert frequency is identified and shown in Figure 8.

Figure 8. Instantaneous frequency of HHT in case of open-circuit fault of switches at K1 and K2

Figure 8 shows that the instantaneous frequency (fsh1=50 Hz) is the frequency that characterizes the open-circuit fault of the IGBT.

4. Conclusions

In this paper, a method for diagnosing and detecting the harmonic characteristic of the open-circuit fault of an IGBT of the two-stage three-phase inverter supplying an induction motor is proposed. This diagnostic method is based on the Hilbert-Huang transform to identify the instantaneous frequency that allows us to detect the frequency characterizing the open-circuit fault of the IGBT. This paper study is based on the extraction of the IMF for the healthy and the IGBT open-circuit fault cases by using the algorithm (CEEMDAN). To detect the open-circuit faults related to the resulting IMF, the Hilbert spectral envelope are conducted to identify the instantaneous frequency. This instantaneous frequency is the frequency characterizing the open-circuit fault of the IGBT. The method proposed is more efficient and more sensitive to the early detection and the diagnosis of open-circuit fault of the IGBTs of the inverter when compared to the conventional methods for example the wavelet or the STFT. The various results obtained are validated by several experimental works carried out in the LDEE laboratory by the diagnostic group to assess the effectiveness and the merits of the proposed HHT approach.

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