Acoustic-Based Fault Diagnosis of Commutator Motor

In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.


Introduction
Fault diagnosis of electrical rotating motors has been extensively investigated since the 20th century, and can increase the reliability and safety of electrical rotating motors.Condition monitoring of electrical motors are very important for industry, reducing loss due to unforeseen faults and damage.Unforeseen faults and damage of electrical rotating motors lead to the loss of production and income.Unfortunately, stator and rotor are the most important components in electrical rotating motors.Stator and rotor faults appear very often.Stators and rotors of electrical rotating motors must be monitored.Condition monitoring guarantees safe operation of machines and prevents unforeseen breakdowns.Acoustic signals contain a lot of diagnostic information, and can be used for detection of faults.Therefore, acoustic signals and signal processing methods should be deeply studied for proper recognition.Scientists developed many diagnostic methods of fault diagnosis.They are used for various types of machines and faults.Faults of electrical rotating motors (stator faults, rotor faults, broken rotor bar, ring cracking, bearing failures, rotor shaft failure, air-gap irregularities, broken teeth on sprocket) can be diagnosed by vibration [1][2][3][4][5][6][7][8][9][10][11][12] and acoustic signals [13][14][15][16][17][18][19][20][21][22].Electric current analysis [23][24][25][26][27][28][29][30][31] and thermal analysis [32][33][34] are mostly used for limited faults, such as stator faults, rotor faults, and bearing failures.Acoustic signals are difficult to process, because they are very noisy (for example, several operating motors generate many acoustic signals).The advantage of acoustic-based fault diagnosis is non-invasive measurement (for example, we can measure an acoustic signal two meters from the machine).Vibration-based fault diagnosis is similar, but we have to put our measuring device close to the machine.Vibration signals are less noisy than acoustic signals.
The paper presents acoustic-based fault diagnosis of the commutator motor (CM).Five states of the commutator motor (CM) were considered: CM with shorted stator coils (Figures 1a and 2a), CM with broken rotor coil (Figures 1b and 2b), healthy CM (Figure 1c), CM with broken tooth on sprocket (Figure 3), CM with damaged gear train (Figure 4).with broken rotor coil (Figures 1b and 2b), healthy CM (Figure 1c), CM with broken tooth on sprocket (Figure 3), CM with damaged gear train (Figure 4).with broken rotor coil (Figures 1b and 2b), healthy CM (Figure 1c), CM with broken tooth on sprocket (Figure 3), CM with damaged gear train (Figure 4).with broken rotor coil (Figures 1b and 2b), healthy CM (Figure 1c), CM with broken tooth on sprocket (Figure 3), CM with damaged gear train (Figure 4).The described approach consists of methods of signal processing, such as amplitude normalization, FFT, the MSAF-15-MULTIEXPANDED-8-GROUPS, nearest neighbour (NN), or nearest mean (NM) or SOM (self-organizing map) or BNN (backpropagation neural network).The paper provided the results of acoustic analysis of the CM.The described approach consists of methods of signal processing, such as amplitude normalization, FFT, the MSAF-15-MULTIEXPANDED-8-GROUPS, nearest neighbour (NN), or nearest mean (NM) or SOM (self-organizing map) or BNN (backpropagation neural network).The paper provided the results of acoustic analysis of the CM.The described approach consists of methods of signal processing, such as amplitude normalization, FFT, the MSAF-15-MULTIEXPANDED-8-GROUPS, nearest neighbour (NN), or nearest mean (NM) or SOM (self-organizing map) or BNN (backpropagation neural network).The paper provided the results of acoustic analysis of the CM.

Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups
The Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups (MSAF-15-MULTIEXPANDED-8-GROUPS) depended on differences of spectra of acoustic signals.Differences of spectra of acoustic signals depended on generated acoustic signals of the CM.Generated acoustic signals depended on type of the motor, motor size, rotor speed, and analysed faults of the motor.The author analysed 5 states of the CM (healthy CM, CM with broken rotor coil, CM with shorted stator coils, CM with broken tooth on sprocket, and CM with damaged gear train).

Nearest Neighbour Classifier
A classification step can be achieved by the nearest neighbour (NN) classifier.This method of classification is well-known.It is used in economics, telecommunication, pattern recognition, fault diagnosis, and image recognition.The NN classifier can classify data (linearly separable and nonlinearly separable) with high recognition rate.The NN is simple to implement, and it requires a few training feature vectors for proper classification.It uses labels for classification of test feature vectors.The classifier uses metric distance to compare two vectors (training and test feature vectors).There are many distance metrics to compare training and test vector.Distance metrics such as Euclidean, Manhattan, and Minkowski had similar results.In this paper, the classification step was carried out using Manhattan distance (1): where  Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44].There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51].The results of recognition depended on number of found essential frequency components and selected classification method.

Nearest Neighbour Classifier
A classification step can be achieved by the nearest neighbour (NN) classifier.This method of classification is well-known.It is used in economics, telecommunication, pattern recognition, fault diagnosis, and image recognition.The NN classifier can classify data (linearly separable and non-linearly separable) with high recognition rate.The NN is simple to implement, and it requires a few training feature vectors for proper classification.It uses labels for classification of test feature vectors.The classifier uses metric distance to compare two vectors (training and test feature vectors).There are many distance metrics to compare training and test vector.Distance metrics such as Euclidean, Manhattan, and Minkowski had similar results.In this paper, the classification step was carried out using Manhattan distance (1): Description of the NN classifier is available in following articles [35,36].

Nearest Mean Classifier
Similar to the NN classifier, the nearest mean (NM) classifier is also based on computed distance.It uses average feature vector instead of training feature vectors.Average feature vector afv is defined as ( 2) where afv-average feature vector, p-number of essential frequency components, and y i -value of essential frequency component with i index.
The nearest distance between test and average feature vector is computed.Next, the label occurring with specific average feature vector is the label for the test feature vector.The NM classifier can classify data with a high recognition rate.For the classification step, the author used Manhattan distance (1).

Self-Organizing Map
The self-organizing map was used for machine learning.The self-organizing map (SOM) is a clustering method.It does not use labels for classification of test feature vectors.The SOM is an unsupervised neural network.It is used for clustering data, meteorology and oceanography, project prioritization, selection, and failure analysis.For example, the SOM is used for meteorological applications such as climate change analysis, precipitation, snow, wind, air temperature, etc.The SOM analysis has been applied for computed feature vectors of acoustic signals.Similarity between feature vectors depends on the location of features on the two-dimensional map (nodes).The SOM uses training step and testing step.In the training step, weights of nodes are changed (at the beginning, values of weights are random).The author used the following self-organizing map (Figure 24).It was implemented in MATLAB.Description of the SOM is available in the following article [37].

Nearest Mean Classifier
Similar to the NN classifier, the nearest mean (NM) classifier is also based on computed distance.It uses average feature vector instead of training feature vectors.Average feature vector afv is defined as (2) where afv-average feature vector, p-number of essential frequency components, and yi-value of essential frequency component with i index.
The nearest distance between test and average feature vector is computed.Next, the label occurring with specific average feature vector is the label for the test feature vector.The NM classifier can classify data with a high recognition rate.For the classification step, the author used Manhattan distance (1).

Self-Organizing Map
The self-organizing map was used for machine learning.The self-organizing map (SOM) is a clustering method.It does not use labels for classification of test feature vectors.The SOM is an unsupervised neural network.It is used for clustering data, meteorology and oceanography, project prioritization, selection, and failure analysis.For example, the SOM is used for meteorological applications such as climate change analysis, precipitation, snow, wind, air temperature, etc.The SOM analysis has been applied for computed feature vectors of acoustic signals.Similarity between feature vectors depends on the location of features on the two-dimensional map (nodes).The SOM uses training step and testing step.In the training step, weights of nodes are changed (at the beginning, values of weights are random).The author used the following self-organizing map (Figure 24).It was implemented in MATLAB.Description of the SOM is available in the following article [37].

Backpropagation Neural Network
Backpropagation neural network (BNN) was also used for machine learning.It is a supervised learning method.It is also a well-known method of data classification.It has been used for many applications, such as speaker recognition, image recognition, signal recognition, control, prediction,

Results of Acoustic-Based Fault Diagnosis Technique of the Commutator Motor
Measurements of acoustic signals of commutator motors were conducted in the workshop.The author measured and analysed 5 states of the CM: healthy CM, CM with shorted stator coils (Figure 26a), CM with broken rotor coil (Figure 26b), CM with broken tooth on sprocket (Figure 27), and CM with damaged gear train (Figure 28).Analysed commutator motors had the following parameters: WoM = 1.84 kg, PoM = 500 W, RSoM = 3000 rpm, VoM = 230 V, foM = 50 Hz, where WoM-weight of the motor, PoM-rated power of the motor, RSoM-rotor speed, VoM-supply voltage of the motor, and foMcurrent frequency of the motor.Layers of BNN had following number of neurons: input layer-28, hidden layer-100, output layer-5.The values of output neurons were 10000-healthy CM, 01000-CM with shorted stator coils, 00100-CM with broken rotor coil, 00010-CM with broken tooth on sprocket, and 00001-CM with damaged gear train.More information about BNN can be found in following papers [38][39][40][41][42][43][44].

Results of Acoustic-Based Fault Diagnosis Technique of the Commutator Motor
Measurements of acoustic signals of commutator motors were conducted in the workshop.The author measured and analysed 5 states of the CM: healthy CM, CM with shorted stator coils (Figure 26a), CM with broken rotor coil (Figure 26b), CM with broken tooth on sprocket (Figure 27), and CM with damaged gear train (Figure 28).Analysed commutator motors had the following parameters: W oM = 1.84 kg, P oM = 500 W, RS oM = 3000 rpm, V oM = 230 V, f oM = 50 Hz, where W oM -weight of the motor, P oM -rated power of the motor, RS oM -rotor speed, V oM -supply voltage of the motor, and f oM -current frequency of the motor.computer games, robots, etc.The applied backpropagation algorithm has been described in the literature [38][39][40][41][42][43][44].The author implemented a backpropagation neural network (Figure 25).Layers of BNN had following number of neurons: input layer-28, hidden layer-100, output layer-5.The values of output neurons were 10000-healthy CM, 01000-CM with shorted stator coils, 00100-CM with broken rotor coil, 00010-CM with broken tooth on sprocket, and 00001-CM with damaged gear train.More information about BNN can be found in following papers [38][39][40][41][42][43][44].

Results of Acoustic-Based Fault Diagnosis Technique of the Commutator Motor
Measurements of acoustic signals of commutator motors were conducted in the workshop.The author measured and analysed 5 states of the CM: healthy CM, CM with shorted stator coils (Figure 26a), CM with broken rotor coil (Figure 26b), CM with broken tooth on sprocket (Figure 27), and CM with damaged gear train (Figure 28).Analysed commutator motors had the following parameters: WoM = 1.84 kg, PoM = 500 W, RSoM = 3000 rpm, VoM = 230 V, foM = 50 Hz, where WoM-weight of the motor, PoM-rated power of the motor, RSoM-rotor speed, VoM-supply voltage of the motor, and foMcurrent frequency of the motor.
where: ERCM-efficiency of recognition of the CM for defined class, NTSCMTP-number of test samples of the CM for defined class tested properly, NATSCM-number of all test samples of the CM for defined class.
Total efficiency of recognition of the CM (TERCM) was introduced to evaluate efficiency of recognition of all states of the CM.It was expressed as (4): where TERCM-total efficiency of recognition of the CM, ERCM1-efficiency of recognition of the healthy CM, ERCM2-efficiency of recognition of the CM with broken rotor coil, ERCM3-efficiency of recognition of the CM with shorted stator coils, ERCM4-efficiency of recognition of the CM with broken tooth on sprocket, and ERCM5-efficiency of recognition of the CM with damaged gear train.Acoustic signal analysis of the CM is presented in Tables 6-9.The acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the NN are presented in Table 6 (28 analysed frequency components).
where: ERCM-efficiency of recognition of the CM for defined class, NTSCMTP-number of test samples of the CM for defined class tested properly, NATSCM-number of all test samples of the CM for defined class.
Total efficiency of recognition of the CM (TERCM) was introduced to evaluate efficiency of recognition of all states of the CM.It was expressed as (4): where TERCM-total efficiency of recognition of the CM, ERCM1-efficiency of recognition of the healthy CM, ERCM2-efficiency of recognition of the CM with broken rotor coil, ERCM3-efficiency of recognition of the CM with shorted stator coils, ERCM4-efficiency of recognition of the CM with broken tooth on sprocket, and ERCM5-efficiency of recognition of the CM with damaged gear train.Acoustic signal analysis of the CM is presented in Tables 6-9.The acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the NN are presented in Table 6 (28 analysed frequency components).
where: E RCM -efficiency of recognition of the CM for defined class, N TSCMTP -number of test samples of the CM for defined class tested properly, N ATSCM -number of all test samples of the CM for defined class.Total efficiency of recognition of the CM (TE RCM ) was introduced to evaluate efficiency of recognition of all states of the CM.It was expressed as (4): where TE RCM -total efficiency of recognition of the CM, E RCM1 -efficiency of recognition of the healthy CM, E RCM2 -efficiency of recognition of the CM with broken rotor coil, E RCM3 -efficiency of recognition of the CM with shorted stator coils, E RCM4 -efficiency of recognition of the CM with broken tooth on sprocket, and E RCM5 -efficiency of recognition of the CM with damaged gear train.Acoustic signal analysis of the CM is presented in Tables 6-9.The acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the NN are presented in Table 6 (28 analysed frequency components).The acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the NM are shown in Table 7 (28 analysed frequency components).
The acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the SOM were presented in Table 8 (28 analysed frequency components).
The acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the BNN were presented in Table 9 (28 analysed frequency components).
The method of feature extraction the MSAF-15-MULTIEXPANDED-8-GROUPS and selected classifiers provided high recognition rates (TE RCM in the range of 88.4-94.6%).
Self-organizing map and backpropagation neural network are trained.The training is different each time.The NN classifier and the NM classifier had the same results each time.Moreover, feature vectors had small differences between them.The NN classifier and the NM classifier were better for the recognition of close feature vectors.

Conclusions
The article presented acoustic-based fault diagnosis technique of the CM.Five states of the CM were considered: healthy CM, CM with broken rotor coil, CM with shorted stator coils, CM with broken tooth on sprocket, and CM with damaged gear train.The method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS was described and implemented.Classifiers NN, NM, SOM, and BNN were used for acoustic analysis of the CM.
Analysed values of TE RCM were in the range of 88.4-94.6%.The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TE RCM = 94.6%.The implementation of the proposed fault diagnosis technique had low cost.Laptop and microphones are available for $300.Other types of rotating electric motors (such as DC motors, synchronous motors, induction motors) can also be diagnosed using acoustic analysis.
The proposed acoustic-based fault diagnosis technique has its limitations.If the motor runs too quietly, it is difficult to use the mentioned technique and microphone.However, the presented acoustic-based fault diagnosis technique is appropriate for acoustic signals of rotating motor and other types of acoustic signals (for example acoustic signal of an engine).The proposed acoustic-based fault diagnosis technique can be extended to detect more complicated faults.Future research will focus on the analysis of new feature extraction methods, other types of faults, and other diagnostic signals, such as vibrations.

Figure 3 .
Figure 3. Broken tooth on sprocket of the electric impact drill.

Figure 3 .
Figure 3. Broken tooth on sprocket of the electric impact drill.

Figure 3 .
Figure 3. Broken tooth on sprocket of the electric impact drill.Figure 3. Broken tooth on sprocket of the electric impact drill.

Figure 3 .
Figure 3. Broken tooth on sprocket of the electric impact drill.Figure 3. Broken tooth on sprocket of the electric impact drill.

Figure 4 .
Figure 4. Damaged gear train of the electric impact drill.
Acoustic-based fault diagnosis technique was based on pattern recognition.It used a preprocessing step, feature extraction step, and classification step.A block diagram of acoustic-based fault diagnosis technique was shown in Figure 5.

Figure 4 .
Figure 4. Damaged gear train of the electric impact drill.
Acoustic-based fault diagnosis technique was based on pattern recognition.It used a pre-processing step, feature extraction step, and classification step.A block diagram of acoustic-based fault diagnosis technique was shown in Figure 5. Electronics 2018, 7, x FOR PEER REVIEW 3 of 21

Figure 4 .
Figure 4. Damaged gear train of the electric impact drill.
Acoustic-based fault diagnosis technique was based on pattern recognition.It used a preprocessing step, feature extraction step, and classification step.A block diagram of acoustic-based fault diagnosis technique was shown in Figure 5.

Figure 5 .
Figure 5. Flowchart of acoustic-based fault diagnosis techniques of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS.Acoustic signals were measured using ZALMAN ZM-MIC1 microphone.The following steps of signal processing were used: recording of acoustic signal of the CM, split of soundtrack into

Figure 6 .
Figure 6.Experimental setup of analysis of acoustic signals of commutator motors.

Figure 6 .
Figure 6.Experimental setup of analysis of acoustic signals of commutator motors.

Figure 6 .
Figure 6.Experimental setup of analysis of acoustic signals of commutator motors.

Figure 20 .
Figure 20.Computed essential frequency components for vector cmbrc (CM with broken rotor coil).

Figure 20 .
Figure 20.Computed essential frequency components for vector cmbrc (CM with broken rotor coil).

Figure 20 .
Figure 20.Computed essential frequency components for vector cmbrc (CM with broken rotor coil).

Figure 22 .
Figure 22.Computed essential frequency components for vector cmbts (CM with broken tooth on sprocket).

Figure 23 .
Figure 23.Computed essential frequency components for vector cmdgt (CM with damaged gear train).

Figure 23 .
Figure 23.Computed essential frequency components for vector cmdgt (CM with damaged gear train).

Figure 27 .
Figure 27.CM with broken tooth on sprocket.

Figure 28 .
Figure 28.CM with damaged gear train.The author used 30 one-second training samples for proper pattern creation process.The author used 500 one-second test samples for proper testing process.Training and test samples of the CM were processed and analysed.The author used technique presented in Section 2 for proper fault diagnosis.Acoustic data of the CM were analysed using efficiency of recognition (E RCM ).It was defined as (3):

Table 1 .
Computed essential frequency components for vector hcm (healthy CM).

Table 2 .
Computed essential frequency components for vector cmbrc (CM with broken rotor coil).

Table 3 .
Computed essential frequency components for vector cmssc (CM with shorted stator coils).

Table 4 .
Computed essential frequency components for vector cmbts (CM with broken tooth on sprocket).

Table 5 .
Computed essential frequency components for vector cmdgt (CM with damaged gear train).
Figure 19.Computed essential frequency components for vector hcm (healthy CM).

Table 2 .
Computed essential frequency components for vector cmbrc (CM with broken rotor coil).

Table 2 .
Computed essential frequency components for vector cmbrc (CM with broken rotor coil).

Table 3 .
Computed essential frequency components for vector cmssc (CM with shorted stator coils).

Table 4 .
Computed essential frequency components for vector cmbts (CM with broken tooth on sprocket).
Figure 21.Computed essential frequency components for vector cmssc (CM with shorted stator coils).Figure 22. Computed essential frequency components for vector cmbts (CM with broken tooth on sprocket).Electronics 2018, 7, x FOR PEER REVIEW 13 of 21

Table 5 .
Computed essential frequency components for vector cmdgt (CM with damaged gear train).

Table 6 .
Acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the NN.

Table 7 .
Acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the NM.

Table 8 .
Acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the SOM.

Table 9 .
Acoustic signal analysis of the CM using the MSAF-15-MULTIEXPANDED-8-GROUPS and the BNN.