Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TED = 96%, TECG-A = 97%, TECG-B = 100%).


Introduction
Today rotating machinery is used for a wide variety of industrial applications such as electrical motors, engines, home appliances and electric power tools. It can also find applications in mining, oil, car, energy, and the steel industry. Cost-effective and non-destructive fault detection is profitable for industry. It can be used for rotating machinery. Reliable operation of rotating machinery is essential for many factories, oil refineries, industrial plants. Gas turbines, motors, pumps, aircraft engines, drive trains can be diagnosed by fault diagnosis techniques. Machines must operate safely without interruptions. If faults occur, the consequences can be catastrophic. Damaged machines generate costs, for example replacement of the machine or stopped production lines in the factory. Thus, the benefits of fault detection are maintenance cost savings.
There are lots of studies in the literature related to fault diagnosis and fault detection of rotating machinery. Analysis of electric currents is developed in the articles [1][2][3][4][5]. The results of current recognition are very good. However it can only be used for limited number of electrical faults such as broken bars, shorted rotors, stator coils. Electric current-based methods are usually useless for many mechanical faults such as damaged teeth on sprockets, faulty gears, faulty fans, etc. The next methods developed in the literature are based on vibration analysis [6][7][8][9][10][11][12][13] and acoustic analysis [14][15][16][17][18][19][20][21][22]. They are very effective. There is no need to connect a measuring sensor with the machine for acoustic-based measurements. Vibration-based measurements require a connection between the sensor measure signals immediately. Vibration and acoustic analysis can also detect mechanical and electrical faults of rotating machinery.
The next method of fault detection is thermal analysis. Thermal analysis methods are described in [23][24][25]. Temperature detection can be performed using thermal imaging cameras, infrared thermometers and portable laser thermometers. If we use a thermal imaging camera or portable laser thermometer, then we can measure from a distance. The next method of fault detection of rotating machinery is oil analysis. It can provide diagnostic information about the condition of rotating machinery. In [26,27] some methods are mentioned: rotating disc electrode spectroscopy, inductively coupled plasma spectroscopy, FPQ-XRF, acid digestion, light blocking, light scattering, laser imaging, laser imaging, ferrography, light blocking, light scattering, laser imaging, fuel sniffer, gas chromatography, gravimetric, Karl Fischer titration, viscosity, etc. Multidimensional prognostics for rotating machinery was also presented [28].
This article describes the application of the acoustic-based approach to an electric impact drill (EID)-Verto 50G515, made in China, and two coffee grinders designated as coffee grinder A (CG-A)-Metrox ME-1497, made in China, and coffee grinder B (CG-B)-Sencor SCG 1050WH, made in China. The EID, CG-A, and CG-B use commutator motors. The commutator motor is a type of electrical motor used for power tools and home appliances such as blenders, coffee grinders and hair driers. The author analysed five electric impact drills (one healthy and four faulty). Each of them generates acoustic signals. Five signals are analysed: healthy ( Figure 1     measure signals immediately. Vibration and acoustic analysis can also detect mechanical and electrical faults of rotating machinery. The next method of fault detection is thermal analysis. Thermal analysis methods are described in [23][24][25]. Temperature detection can be performed using thermal imaging cameras, infrared thermometers and portable laser thermometers. If we use a thermal imaging camera or portable laser thermometer, then we can measure from a distance. The next method of fault detection of rotating machinery is oil analysis. It can provide diagnostic information about the condition of rotating machinery. In [26,27] some methods are mentioned: rotating disc electrode spectroscopy, inductively coupled plasma spectroscopy, FPQ-XRF, acid digestion, light blocking, light scattering, laser imaging, laser imaging, ferrography, light blocking, light scattering, laser imaging, fuel sniffer, gas chromatography, gravimetric, Karl Fischer titration, viscosity, etc. Multidimensional prognostics for rotating machinery was also presented [28].
This article describes the application of the acoustic-based approach to an electric impact drill (EID)-Verto 50G515, made in China, and two coffee grinders designated as coffee grinder A (CG-A)-Metrox ME-1497, made in China, and coffee grinder B (CG-B)-Sencor SCG 1050WH, made in China. The EID, CG-A, and CG-B use commutator motors. The commutator motor is a type of electrical motor used for power tools and home appliances such as blenders, coffee grinders and hair driers. The author analysed five electric impact drills (one healthy and four faulty). Each of them generates acoustic signals. Five signals are analysed: healthy ( Figure 1                                           In Section 1, the author presents a review of the fault detection methods. In Section 2, the author describes the acoustic based approach and proposed methods of signal processing. In Section 3, the recognition results of the EID, CG-A, and CG-B are presented. A discussion is presented in Section 4. In Section 5, summary and conclusions are described.

Developed acoustic based approach
The developed acoustic-based approach used signal processing methods and the acoustic data of the EID, CG-A and CG-B. Acoustic data were obtained using a HAMA 00057152 microphone. The parameters of the microphone are: frequency response 30-16,000 Hz, rated impedance 1,400 Ω, sensitivity −62 dB. The microphone was placed 0.2-0.3 m away from the EID, CG-A and CG-B. Other types of microphones could be also used. Acoustic data were split (using "MPlayer library-The Movie Player"-wav file parameters sampling frequency 44,100 Hz, single channel, 16 bits resolution, stationary signal) and normalized. Normalization of amplitude divided each sample (in the time domain) by the maximum value of the signal (in time domain). After that feature vectors were formed using the RMS or MSAF-17-MULTIEXPANDED-FILTER-14 (the methodology is presented in Section 2.1). Next the Nearest Neighbour (NN) classifier compared feature vectors in the classification step. The developed acoustic based approach is shown in Figure 14.   In Section 1, the author presents a review of the fault detection methods. In Section 2, the author describes the acoustic based approach and proposed methods of signal processing. In Section 3, the recognition results of the EID, CG-A, and CG-B are presented. A discussion is presented in Section 4. In Section 5, summary and conclusions are described.

Developed acoustic based approach
The developed acoustic-based approach used signal processing methods and the acoustic data of the EID, CG-A and CG-B. Acoustic data were obtained using a HAMA 00057152 microphone. The parameters of the microphone are: frequency response 30-16,000 Hz, rated impedance 1,400 Ω, sensitivity −62 dB. The microphone was placed 0.2-0.3 m away from the EID, CG-A and CG-B. Other types of microphones could be also used. Acoustic data were split (using "MPlayer library-The Movie Player"-wav file parameters sampling frequency 44,100 Hz, single channel, 16 bits resolution, stationary signal) and normalized. Normalization of amplitude divided each sample (in the time domain) by the maximum value of the signal (in time domain). After that feature vectors were formed using the RMS or MSAF-17-MULTIEXPANDED-FILTER-14 (the methodology is presented in Section 2.1). Next the Nearest Neighbour (NN) classifier compared feature vectors in the classification step. The developed acoustic based approach is shown in Figure 14. In Section 1, the author presents a review of the fault detection methods. In Section 2, the author describes the acoustic based approach and proposed methods of signal processing. In Section 3, the recognition results of the EID, CG-A, and CG-B are presented. A discussion is presented in Section 4. In Section 5, summary and conclusions are described.

Developed Acoustic Based Approach
The developed acoustic-based approach used signal processing methods and the acoustic data of the EID, CG-A and CG-B. Acoustic data were obtained using a HAMA 00057152 microphone. The parameters of the microphone are: frequency response 30-16,000 Hz, rated impedance 1400 Ω, sensitivity −62 dB. The microphone was placed 0.2-0.3 m away from the EID, CG-A and CG-B. Other types of microphones could be also used. Acoustic data were split (using "MPlayer library-The Movie Player"-wav file parameters sampling frequency 44,100 Hz, single channel, 16 bits resolution, stationary signal) and normalized. Normalization of amplitude divided each sample (in the time domain) by the maximum value of the signal (in time domain). After that feature vectors were formed using the RMS or MSAF-17-MULTIEXPANDED-FILTER-14 (the methodology is presented in Section 2.1). Next the Nearest Neighbour (NN) classifier compared feature vectors in the classification step. The developed acoustic based approach is shown in Figure 14. An experimental setup consisted of the microphone and a computer. It was used to analyse the electric impact drill/coffee grinder ( Figure 15a). Measurement of acoustic signals is depicted in Figure 15b.  An experimental setup consisted of the microphone and a computer. It was used to analyse the electric impact drill/coffee grinder ( Figure 15a). Measurement of acoustic signals is depicted in Figure 15b. An experimental setup consisted of the microphone and a computer. It was used to analyse the electric impact drill/coffee grinder ( Figure 15a). Measurement of acoustic signals is depicted in Figure 15b.
If there are no CFCs, then set a parameter ToCFCs. The parameter is defined as Equation (1): Let's analyse the following example: three training sets are given. Each of them has four training samples. Eighteen differences are computed (six for the first training set, six for the second training set, six for the third training set). Let's suppose that frequency component 130 Hz is found three times for |h − f|. Let's suppose that frequency components 110, 160 Hz are found two times for |h − s|. Let's suppose that frequency components 110, 140 Hz are found two times for |f − s|. Let's suppose that frequency component 500 Hz is found three times for |b − h|. Let's suppose that frequency components 600, 610 Hz are found two times for |b − f|. Let's suppose that frequency components 600, 710 Hz are found two times for |b − s|. There are no CFCs. In other words, we can say that: 17-means that, we analyse 17 (local) maximum values of analysed difference between FFT spectra of acoustic signals, for example |h − f|, 14-means that, we set 14 Hz frequency bandwidth, for example for frequency 50 Hz it will be <50 − 7 Hz, 50 + 7Hz>.
A block diagram of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 is presented in Figure 16.                              Figures (27-29). The value of the parameter ToCFCs was equal to 0.5 for the CG-A. The MSAF-17-MULTIEXPANDED-FILTER-14 selected two frequency bandwidths of the CG-A: <515-537 Hz>, <1560-1575 Hz>. The selected frequency bandwidths/features of the CG-A are depicted in Figures (27-29). The value of the parameter ToCFCs was equal to 0.5 for the CG-A.   The MSAF-17-MULTIEXPANDED-FILTER-14 selected two frequency bandwidths of the CG-A: <515-537 Hz>, <1560-1575 Hz>. The selected frequency bandwidths/features of the CG-A are depicted in Figures (27-29). The value of the parameter ToCFCs was equal to 0.5 for the CG-A.    Figures (30-31). The value of the parameter ToCFCs was equal to 0.5 for the CG-B.   Figures (30-31). The value of the parameter ToCFCs was equal to 0.5 for the CG-B.  Next computed features were classified. To classify features the NN classifier [29][30][31] was used (please see Section 2.3). There are 145 features in the feature vector. It can be noticed that distance classifiers (for example: k-means, Nearest Mean) should have also good results. Fuzzy classifiers [32] and neural network [33][34][35] can be also suitable for the acoustic-based approach. The NN classifier was selected because of its good recognition efficiency for multi-dimensional vectors.

RMS
The second method of feature extraction used for the proposed acoustic based approach is the Root Mean Square (RMS). The RMS is a well-known method for feature extraction. It is defined as Equation (2) Next computed features were classified. To classify features the NN classifier [29][30][31] was used (please see Section 2.3). There are 145 features in the feature vector. It can be noticed that distance classifiers (for example: k-means, Nearest Mean) should have also good results. Fuzzy classifiers [32] and neural network [33][34][35] can be also suitable for the acoustic-based approach. The NN classifier was selected because of its good recognition efficiency for multi-dimensional vectors.

RMS
The second method of feature extraction used for the proposed acoustic based approach is the Root Mean Square (RMS). The RMS is a well-known method for feature extraction. It is defined as Equation (2):  The values of the RMS of acoustic signals "Healthy EID" and "EID with a rear ball bearing fault" were similar. It will be difficult to recognise these two classes. In the presented analysis (please see Section 3) the author used 50 1-s samples for each class of the CG-A. Two hundred 1-s samples were used for four classes (of the CG-A). There were x RMS251 , ..., x RMS300 -RMS values of the healthy CG-A, x RMS301 , ..., x RMS350 -RMS values of the CG-A with a heavily damaged rear sliding bearing, x RMS351 , ..., x RMS400 − RMS values of the CG-A with a damaged shaft and heavily damaged rear sliding bearing, x RMS401 , ..., x RMS450 -RMS values of the motor off (CG-A off). The values x RMS401 , ..., x RMS450 were the same as RMS values of the EID with a shifted brush (EID off). The computed RMS values of the CG-A are presented in Tables 6-8. The values of the RMS of acoustic signals "CG-A with a heavily damaged rear sliding bearing" and "CG-A with a damaged shaft and heavily damaged rear sliding bearing" were similar. It will be difficult to recognise these two classes.
In the presented analysis (please see Section 3) the author used 50 1-s samples for each class of the CG-B. One hundred and fifty 1-s samples were used for three classes (of the CG-B). There were x RMS451 , ..., x RMS500 -RMS values of the healthy CG-B, x RMS501 , ..., x RMS550 -RMS values of the CG-B with a light damaged rear sliding bearing, x RMS551 , ..., x RMS600 -RMS values of the motor off (CG-B off). The values x RMS551 , ..., x RMS600 were the same as RMS values of the EID with a shifted brush (EID off). The computed RMS values of the CG-B are presented in Tables 9 and 10.

NN Classifier
The NN classifier is very known in the literature [29][30][31]. This type of a classifier is based on lazy learning. It does not generalize the training data. Each training feature vector has a label with a class (ID of the class). The label (ID of the class) is given to the feature vector in the training phase.
An unlabeled test feature vector is used in the classification (testing) phase. The NN classifier assigns the label, which is the closest to the training data. For this reason, distance metric is used. The author used Euclidean distance, although other distance functions could be used. Similar results were obtained using other distance functions (Manhattan distance and Minkowski distance). Euclidean distance was defined as Equation (3): where x-test feature vector, y-training feature vector, ED(x−y)-Euclidean distance, n-number of features (it is 1 feature for the RMS). The NN classifier is useful for classification of feature vectors. It was found application in pattern recognition, speaker recognition, image recognition, text recognition, face recognition etc. The NN classifier is described in detail in [29][30][31].

Recognition Results of the EID, CG-A, CG-B
The analysed EID was powered from the 230 V/50 Hz mains. The author used 50G515 electric impact drills. Other devices could be used. It generated five acoustic signals denoted as: healthy EID, EID with 15 broken rotor blades (faulty fan), EID with a bent spring, EID with a shifted brush (motor off), EID with a rear ball bearing fault. Measurements were carried out in the room 3 m × 3 m. The analysed EID had rated power P D = 500 W, rotation speed R D = 3000 rpm and weight M D = 1.84 kg.
The analysed CG-A was also powered from the 230 V/50 Hz mains. The author used a ME-1498 coffee grinder. Other devices could be used. The analysed CG-A consisted of a FY5420 motor (rated power 140 W). It had rotor speed of 28,000-30,000 rpm. It generated four acoustic signals denoted as: healthy, with a slightly damaged rear sliding bearing, with a moderately damaged rear sliding bearing, motor off.
The analysed CG-B was also powered from the 230 V/50 Hz mains. The author used a SCG 1050WH coffee grinder. The analysed CG-B consisted of a HC5420 motor (rated power 150 W). It had a rotor speed of 11,300 rpm. It generated three acoustic signals denoted as: healthy, with a light damaged rear sliding bearing, motor off.
Patterns The efficiency of the proposed approach was evaluated using Equation (4). This Equation (4) defined the efficiency of recognition of the EID (E D ): where: E D1 -the efficiency of recognition for D1 class (in the analysis it is one of five classes, for example healthy EID), N D1 -the number of test samples classified as D1 class, N ALL-D1 -the number of all test samples in D1 class. The values of E CG-A and E CG-B were computed similarly to E D1 . The total efficiency of recognition of all classes (TE D ) was also introduced. It was defined as follows Equation (5): where TE D -the total efficiency of recognition of all classes (five states of the EID), E D1 -the efficiency of recognition for D1 class (in the presented analysis D1 class-healthy EID), E D2 -the efficiency of recognition for D2 class (in the presented analysis D2 class-EID with a bent spring), E D3 -the efficiency of recognition for D3 class (in the presented analysis D3 class-EID with 15 broken rotor blades), E D4 -the efficiency of recognition for D4 class (in the presented analysis D4 class-EID with a shifted brush), E D5 -the efficiency of recognition for D5 class (in the presented analysis D5 class-EID with a rear ball bearing fault). The values of TE CG-A and TE CG-B were computed similarly to TE D . Four acoustic signals were used for TE CG-A . Three acoustic signals were used for TE CG-B . The computed values of E D and TE D were presented in Tables 11 and 12. Acoustic signals of the EID were processed by the MSAF-17-MULTIEXPANDED-FILTER-14 method and the NN classifier (Table 11). Table 11. Computed values of E D and TE D of the EID using the MSAF-17-MULTIEXPANDED-FILTER-14 method and the NN classifier.

Type of Acoustic Signal E D (%)
Healthy EID 100 EID with a bent spring 92 EID with (15 broken rotor blades) faulty fan 100 EID with shifted brush (motor off) 100 EID with rear ball bearing fault 88 TE D (%) Total efficiency of recognition of the EID 96 Table 12. Computed values of E D and TE D of the EID using the RMS and the NN classifier.

Type of Acoustic Signal E D (%)
Healthy EID 56 EID with a bent spring 100 EID with (15 broken rotor blades) faulty fan 100 EID with shifted brush (motor off) 100 EID with rear ball bearing fault 60 TE D (%) Total efficiency of recognition of the EID 83.2 Acoustic signals of the EID were processed by the RMS and NN classifier (Table 12).
The computed values of E D and TE D of the proposed approach were following: E D = 88-100%, TE D = 96% for the MSAF-17-MULTIEXPANDED-FILTER-14 method and E D = 56-100%, TE D = 83.2% for the RMS. The computed values of E CG-A and TE CG-A were presented in Tables 13 and 14. Acoustic signals of the CG-A were processed by the MSAF-17-MULTIEXPANDED-FILTER-14 method and the NN classifier (Table 13). Table 13. Computed values of E CG-A and TE CG-A of the CG-A using the MSAF-17-MULTIEXPANDED-FILTER-14 method and the NN classifier.

Type of Acoustic Signal E CG-A (%)
Healthy CG-A 100 CG-A with a heavily damaged rear sliding bearing 100 CG-A with a damaged shaft and heavily damaged rear sliding bearing 88 Motor off 100 TE CG-A (%) Total efficiency of recognition of the CG-A 97 Table 14. Computed values of E CG-A and TE CG-A of the CG-A using the RMS and the NN classifier.

Type of Acoustic Signal E CG-A (%)
Healthy CG-A 100 CG-A with a heavily damaged rear sliding bearing 92 CG-A with a damaged shaft and heavily damaged rear sliding bearing 92 Motor off 100 TE CG-A (%) Total efficiency of recognition of the CG-A 96 Acoustic signals of the CG-A were processed by the RMS and NN classifier ( Table 14).

Type of Acoustic Signal E CG-B (%)
Healthy CG-B 100 CG-B with a light damaged rear sliding bearing 100 Motor off 100 TE CG-B (%) Total efficiency of recognition of the CG-B 100 Table 16. Computed values of E CG-B and TE CG-B of the CG-B using the RMS and the NN classifier.

Type of Acoustic Signal E CG-B (%)
Healthy CG-B 100 CG-B with a light damaged rear sliding bearing 100 Motor off 100 TE CG-B (%) Total efficiency of recognition of the CG-B 100 Acoustic signals of the CG-B were processed by the RMS and NN classifier (Table 16).
The computed values of E CG-B and TE CG-B of the proposed approach were following: E CG-B = 100%, TE CG-B = 100% for the MSAF-17-MULTIEXPANDED-FILTER-14 method and RMS.

Discussion
The acoustic-based fault-detection technique is significant for the recent research area of electrical motors. This approach is useful for inspection of motor condition. It can analyse acoustic signals in places with limited or no access. The novelty of the proposed work was to detect faults of an EID and two coffee grinders. The author focused on feature extraction of five acoustic signals of the EID, four acoustic signals of the CG-A and three acoustic signals of the CG-B. The method MSAF-17-MULTIEXPANDED-FILTER-14 was developed and described. One of the difficulties to solve was selection of training samples. It can be noticed that the recognition results depended on selected training samples. All samples is measured by one microphone. If the acoustic signal is measured by another type of microphone, then it can cause errors of recognition. The proposed acoustic-based approach should use one type of microphone for training as well as testing.
The second of the difficulties to solve was the testing (classification) of a new unknown test samples. It is difficult to recognize, for example, the acoustic signal of a car if we have training samples of an EID. To solve this problem the proposed acoustic-based approach used the NN classifier. The NN classifier found the nearest feature vector (analysed frequency bandwidths). If the acoustic signal of the car is measured, then it will be recognised as an unknown state of the EID. The training set consisted of acoustic signals of the EID and several unknown sounds of cars, ships, helicopters, animals, etc.
It can be noticed that the RMS was very good for recognition of acoustic signals of the EID with a shifted brush (motor off). This class of acoustic signal should be detected by the RMS. However, the RMS method was not good for similar sound intensity level values. The classes of acoustic signals "Healthy EID" and "EID with a rear ball bearing fault" had low values of TE D . The classes of acoustic signals "CG-A with a heavily damaged rear sliding bearing" and "CG-A with a damaged shaft and heavily damaged rear sliding bearing" had lower values of TE CG-A . The MSAF-17-MULTIEXPANDED-FILTER-14 method was good method of feature extraction for all analysed classes of acoustic signals.

Summary and Conclusions
This paper presented fault-detection techniques for an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. Measurements of the acoustic signals of the EID, CG-A, and CG-B were carried out using a microphone. Five signals of the EID were analysed: healthy EID, EID with 15 broken rotor blades (faulty fan), EID with a bent spring, EID with a shifted brush (motor off), EID with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy CG-A, CG-A with a heavily damaged rear sliding bearing, CG-A with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy CG-B, CG-B with a light damaged rear sliding bearing, motor off.
Methods such as RMS, MSAF-17-MULTIEXPANDED-FILTER-14 were used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 was also developed and described in the paper. The classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis was carried out. The computed values of E D and TE D of the proposed approach were following: The acoustic-based analysis was inexpensive. The experimental setup consisted of a microphone and computer. It cost about $500. Pros of this solution are instant measurement and online monitoring of the motor. Cons of this solution are the higher cost and size of the computer. The developed acoustic-based approach has many applications, for example in home and industrial appliances for fault detection. It can be used for electrical motors, engines, machinery and electric power tools [36][37][38][39][40][41][42]. It can also find applications in mining, oil, car, energy, and the steel industry. It can analyse acoustic signals in places with limited or no access. However, the proposed acoustic-based approach has one limitation. It cannot work for a machine that does not generate acoustic signals. Background noises can be also problem, if we analyse several motors in one place and at the same time.
In the future, the proposed acoustic-based approach can be further developed. Other faults of commutator motors can be added to an acoustic signal database. Measurements can be carried out using acoustic cameras and microphone arrays. Vibration-based methods can be added to the fault detection system of commutator motors. New feature extraction methods can also be developed in the future.
Funding: This research was funded by the AGH University of Science and Technology, grant No. 11.11.120.714.