Vibration Monitoring of Electric Generators without Sensor Dedicated

Petronio Vieira Junior*, Miguel Angel Sanz Bobi**, Cristiane Ruiz Gomes*, Hermínio Simões Gomes*, Miguel

Pacheco do Nascimento* *Federal University of Pará

Augusto Corrêa, 1, ZIP 66.075-900, Belém / Pará – Brazil **Pontificia Comillas University

Santa Cruz de Marcenado, 26, ZIP 28.015, Madrid / Spain petronio@ufpa.br, masanz@dsi.icai.upcomillas.es, cris.ruiz.gomes@gmail.com, hermínio@ufpa.br,

miguelpacheco2025@yahoo.com

Abstract - This paper describes a method for on-line condition monitoring of rotating electric generators without a need for installed sensors dedicated. This sensorless method has been based on continuous monitoring vibration using neural networks for the classification of bearing failures experimentally induced. The results obtained have an error of less than 0,5% confirming the validity of the method proposed.

I. INTRODUCTION

Rotating electric generators suffer mechanical wear throughout their useful cycle of life. This kind of failure mode causes an increase in vibrations and oscillations that also are the cause of irregular heating in the rotation shaft, defects in the bearings, displacement of the generator poles, eccentricity, among others problems. These symptoms are monitored by sensors installed in important equipment [1], [2], and, if they appear, this continuous monitoring allows for a programmed shutdown of the equipment minimizing the cost of this failure mode. A common method used for monitoring the eccentricity in the rotor of an electric generator is based on the measurement of the air-gap distance between shaft and support by the installation of proximity sensors embedded in the poles of the machine. In this case, these sensors are mounted in locations of difficult access, demanding a lot of time for their installation, reducing the readiness of the machine and requiring an important investment [3].

This paper presents a new sensorless monitoring methodology able to identify and to correlate faults in the generator using indirect measurements. The method is based on Principal Component Analysis (PCA) for data mining and on Artificial Neural Networks (ANN) for the identification of the defect. The method proposed is low cost, does not interrupt the operation of the machine for sensor installation, minimizes the maintenance of the monitoring system, makes it possible to validate the readings of the dedicated sensor existent and, therefore, provides more accurate and reliable information for maintenance. In order to prove this methodology, experimental tests have been done monitoring the behavior of an electrical generator with failures induced in its bearing. The defect in the bearing was chosen because is difficult the detection of its failures such as eccentricity. A large number of published articles cover the topic about

diagnosis of bearing faults through different strategies such as detection and analysis of vibration in induction machines [4], [5], using stator current [6] and relatively fewer for synchronous machines [3] using voltage generated. A common research approach for diagnosis of faults in electrical machines is Motor Current Signature Analysis (MCSA) but for generators this current contains noise originatied from the load that can be no-lineal and to filter them is difficult.

II. PRELIMINARY ANALYSIS

In order to identify defects in electric generators based on a sensorless method, it is necessary to define the essential variables to be measured. In general, a rotating electrical generator has the following types of variables accessible:

- electrical variables such as voltage and current - mechanical variables such as vibration, torque and

rotation speed - other variables such as temperature and other less

significant measurements The main question to answer is which of them should be

used in order to extract enough information to identify defects. This paper is based on the fact that the magnetic flow produced by a rotating electric generator during the electromechanical conversion of energy, depends on the distances of the air-gap between shaft and support, and therefore, its variation has an important influence on the production of the magnetomotive force (MMF). A consequence of this assessment is the possibility of identification of mechanical faults through the continuous monitoring of electric variables.

The methodology proposed in this paper for fault identification in rotating electrical generators consists of one part rooted in mathematics and computational algorithms, and another one based on experiments. The method proposed is based on the knowledge obtained from a Principal Component Analysis (PCA) and on a modeling using Artificial Neural Networks (ANN). LABVIEW and MATLAB are the software used respectively to acquire and process the data. The process for data acquisition and

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validation of the methodology proposed was performed at the laboratory using the system called Dynamic Bench (DB). The project was financed by ELETRONORTE through the project entitled ‘Monitoring of Hydro Generator MHACE’.

III. FAULT IDENTIFICATION TECHNIQUES BASED ON ANN

Two main methods for fault identification exist: methods based on mathematical models and methods based on knowledge [7]. The mathematical methods use models consisting of equations representing the physical system. Some of these methods are based on analytical redundancy similar to monitoring the measurement supplied by a virtual or software sensor. The methods based on knowledge and heuristics use knowledge based techniques and other artificial intelligence techniques such as ANN. ANN is one of those techniques most often used in fault identification. ANN applications in electrical machines can be found in several references such as [8] where an ANN is used for on-line monitoring of induction motors. In [9] a diagnostic technique using an ANN for rotating mechanical systems is presented. In [10] another application using ANN is described in order to interpretate dissolved gas-in-oil analysis (DCA). The reference [11] presents a fault identification technique and severity analysis for dynamic systems using ANNs and statistical criteria. The wide use of ANNs in different applications is due mainly to their capability to model nonlinear characteristics, their ability for generalization, their easy adaptability, and for their well-known ability in pattern recognition.

IV. EXPERIMENTATION

In order to verify the methodology proposed in this paper a facility for experimentation was built. This facility is based on an experimental bench consisting of three parts: the first part corresponds to the machines, another to the instrumentation and control, and the last part is a resistive load. All of these parts are shown in figure 1.

Fig. 1 - Front view corresponding to the experimental bench.

The part corresponding to the machines possesses a DC motor operating as the primary machine of an electrical generator. The instrumentation and control part of the bench is composed by electronic drives, signal conditioners, sensors and a data acquisition system.

Through this facility called Dynamic Bench (DB), it is

possible to monitor several operation conditions of the generators, not only in normal operation conditions, but also in fault conditions including heating due to harmonics. A DB diagram of blocks is presented in figure 2.

Fig. 2 – Block diagram of the DB.

The machine used as a generator is shown in figure 3. This

is a typical motor-generator of which the main characteristics are the following: 12kW, 230V, 4 poles and 1800 rpm.

Fig. 3 - Primary Machine (Motor DC) and Test Machine (Generator) The DC motor of the DB is controlled by an electronic

converter AC-DC working in the four quadrants of the converter. The generator feeds a resistive load through a power inverter. The power inverter controls the armature current of the generator through the voltage control applied in the resistive load, being able to, for example, cause load rejection.

The load used is purely resistive. The power inverter allows for the introduction of harmonics in the circuit. It is observed as an inductive load.

V. INSTRUMENTATION

Besides the equipment already mentioned, the DB have installed six different types of sensors for the measurement of voltage, current, vibration, torque, temperature and proximity.

Eight accelerometers are installed for vibration measurement. They are shown in figure 4a and located in the generator for monitoring longitudinal and axial vibrations. Each one is connected to a numbered communication channel of a printed circuit board (PCB) of a 19" rack for signal conditioning. This is presented in figure 4b.

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(a) Sensor of vibration (accelerometer)

(b) Rack with PCBs for signal conditioning

Fig. 4 –Acquisition of the vibration signals Sensors of Hall-Effect type were used to acquire the

voltage and current measurements, later processed in order to obtain their Fourier decomposition. These sensors were installed in each electrical phase as shown in figure 5, and the signals were acquired through an acquisition PCB developed at the laboratory.

The sensor of the torque was installed in between the motor and generator axes through an elastic coupling. The proximity sensors were installed close to the shaft of the machines. They make it possible to obtain measurements of the axe displacements.

VI. INTRODUCTION OF MECHANICAL FAULTS

Four types of mechanical faults were introduced in the bearings located on the back and front cover of the generator in order to obtain data corresponding to the operation of the generator working with faults. These faults are the following:

D1: Defect in the Track Internal; D2: Defect in the Track External; D3: Defect in the bearing balls D4: Defect in the Cage.

The bearing used in the experiment is shown in figure 6. Measurements of voltage and vibration were acquired for each fault.

Fig. 5 – PCBs for conditioning of voltage and current sensors Hall-Effect type.

Fig. 6 – Group of bearings of the front and back cover with defects.

VII. ANALYSIS OF THE VARIABLES ACQUIRED

Any variation of the load caused for a variation of the current and for this reason it would not be an appropriate variable for monitoring the generator operation. On the other hand, the comparative analysis of voltage and vibration measurements in normal and fault conditions indicate that the voltage is appropriate for the identification of faults, because it is very characteristic for each normal operation condition.

This is an expected assessment because the voltage measured depends on the magnetic flux that also depends on the air-gap reluctance.

A. Normal operation condition Two sets of data were processed and analyzed

corresponding to 1000 samples of the generator voltage at the same working point for normal condition of the generator. The files were named N1 and N2. Both sets of data were overlapped for a better comparison. Figure 7 shows the voltage overlapping corresponding to the files of data N1 and N2 of one of the phases. The information about the voltages presented in figure 7 suggests that both voltages are similar and that a pattern for the normal operation condition could be developed.

B. Comparison of measurements In order to compare the measurements corresponding to

normal and failed operation conditions, the overlapping of these measurements was analyzed.

Figures 8 and 9 illustrate respectively the overlapping of the voltage and vibration measurements taken without and with failure, files named N1 and D1, respectively.

Fig. 7 – Overlapping of voltages measured N1 and N2.

Fig. 8 –Overlapping of voltages in the phase vab. The data correspond to the files N1 without failure and with failure D1. It is possible to conclude from figures 8 and 9 that the amplitude of the variables observed in the case of a fault condition is larger than the one corresponding to a normal condition. This indicates that the whole system is vibrating with larger intensity.

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Fig. 9 –Overlapping of vibrations in the generator. The data correspond to the files N1 without failure and with failure D1.

A harmonic decomposition of the original data collected

was done in order to obtain patterns for identification of faults. The harmonic decompositions of the voltages without and with a fault operation conditions had minimum differences, having only appeared in the sub-harmonics.

In another way, the harmonic decompositions of the vibrations in operation conditions without and with a fault are very different, showing important frequencies not observed before. Figures 10 to 13 show different harmonic decompositions for different working conditions of the generator.

The harmonic decompositions in the case of the faults D1 and D2 (figures 11 and 12) that are internal defects of the bearing, present a great amount of energy at high frequencies. This characteristic of the vibration measurement can be studied through a demodulation technique based on the Hilbert transformed known as the envelope technique [12].

Fig. 10 – Harmonic decomposition of the generator vibration for normal operation condition.

Fig. 11 – Harmonic decomposition of the generator vibration with failure D1.

Fig. 12 – Harmonic decomposition of the generator vibration with failure D2.

Fig. 13 – Harmonic decomposition of the generator vibration with failure D3.

VIII. PROGRAM DEVELOPED FOR PRINCIPAL COMPONENTS ANALYSIS

In order to obtain patterns for a better recognition between the voltages measured in the case of normal and faulted operation condition, a PCAs analysis was developed. A software application was used for the calculation of the PCAs. Their inputs are matrixes of dimension 1000×11 containing amplitude and phase of voltage and vibration measurements. The illustration in a bar graph indicate the percentage of explanation of the data in function of the auto values of correlation matrix obtained of the curt off of the matrix with values de voltage and vibration. Tables I and II contain the auto values of correlation matrix for normal and for failure operation condition respectively. These tables show the five most significant auto values.

TABLE I

Auto values of the correlation matrix R corresponding to four data sets of voltage for normal operation condition of the generator.

N1 N2 N3 N4 2.1132 2.0417 2.4683 2.1617 1.7497 1.8521 1.8622 1.9345 1.5053 1.5040 1.5274 1.5089 1.4071 1.4923 1.4369 1.4876 1.2339 1.2979 0.9843 1.1204 0.9845 0.9715 0.8970 0.8804 0.9106 0.8503 0.6275 0.6395 0.6999 0.6740 0.5714 0.6025 0.4049 0.3262 0.3481 0.3570 0.0016 0.0007 0.2876 0.3184 0.0002 0.0002 0.0003 0.0002

Figure 14 illustrates the explanation percentage of the

PCAs for the voltage measurements B1, B2, B3 and B4, taken in normal operation condition of the generator. It is possible to observe that just the five larger auto values explain approximately 70% of the data.

TABLE II

Auto values of the correlation matrix R corresponding to data sets of voltage for four faults introduced in the bearing of the generator. D1 D2 D3 D4

2.8946 2.0551 2.5634 1.6569 1.3830 1.4575 1.3527 1.2807 1.2142 1.1562 1.3134 1.2779 1.0694 1.0639 1.0830 1.0980 0.9843 0.9822 0.9854 1.0728 0.9537 0.9541 0.9388 1.0277 0.9067 0.9166 0.8757 0.9729 0.8559 0.9060 0.7783 0.9575 0.6200 0.8124 0.5870 0.9053 0.0986 0.6557 0.4825 0.6826 0.0305 0.0513 0.0508 0.0787

(a) File B1. (b) File B2.

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(c) File B3. (d) File B4.

Fig. 14 – Explanation percentage for normal operation conditions of four data sets of voltage measurements.

(a) File D1. (b) File D2.

(c) File D3. (d) File D4.

Fig. 15 – Explanation percentage for faulted operation conditions. Using the same application of PCAs in order to analyze

the measurements taken when the faults D1, D2, D3 and D4 were introduced in the generator bearing, it was also observed that the five larger auto values explain 70% of the data analyzed. Figure 15 shows this explanation. The analysis of PCAs in these cases confirms that patterns could be obtained for a later identification of faults

IX. FAULT RECOGNITION USING NEURAL NETWORKS

The eleven original measurements collected for monitoring the generator condition were passed through a PCAs analysis. PCAs were obtained explaining about 70% of the data using the five most significant auto values, as a result a matrix of dimension 1000×5 was obtained for each variable measured. This resulting matrix was used as input of an ANN in order to recognize patterns of the generator performance.

The ANN used was developed using the MATLAB neural network toolbox using a training method based on the backpropagation resilient (RPROP). Its architecture used three hidden layers of 4-4-1 neurons. Its inputs were 5 corresponding to the matrices resulting from the PCAs analyses, and its output corresponds to the pattern to be recognized.

The data set used for training the ANN was selected from the files N1 and N3 that correspond to a normal operation condition.

The ANN test was done using data from the files N2 and N4, both also corresponding to a normal operation condition. The results were very good with a RMS error value around 10-3.

Also, the ANN was tested using data coming from the four failures introduced in the generator bearings. The network outputs were distant from the objective in about 0,2 to 0,3, and RMS error of approximately 0,35%. This proved the ability of the ANN to identify patterns of normal condition operation and to distinguish them from generator operation conditions with failures. In conclusion, the ANN is able to differentiate the generator working states using as inputs the results of the PCA analysis from real data collected from the DB.

Concisely the network is capable to differentiate the states of the generator starting from the data of PCAs, through the proximity of the output values with the values objective (0,5). The output of NN is shown in following.

% Output for training: Data known for NN 0.5000 0.5001 0.4996 0.4970 0.5000 0.4998 0.4995 0.4996 0.4995 0.5000 0.4997 0.4994 0.4985 0.4971 0.4987 0.4999 0.4986 0.4992 0.4996 0.5000 0.5004 0.4980 0.4990 0.5001 0.5000 0.4995 0.4992 0.4998 0.4986 0.4994 0.5000 0.4999 0.4985 0.4993 0.4997 0.4992 0.4997 0.4994 0.4999 0.5000 0.4981 0.4995 0.4985 0.4994 0.4983 0.5000 0.5000 0.4987 0.4946 0.4998 erro_rms_normal = 0.0013 % Output of Validation: Data of normal condition of operation that were unknown for NN. 0.4997 0.4898 0.4984 0.4977 0.5004 0.4969 0.5000 0.5000 0.4969 0.4940 0.4890 0.5003 0.4994 0.4981 0.5002 0.5003 0.5001 0.4910 0.4947 0.4986 0.4998 0.4976 0.5001 0.4976 0.4848 0.4988 0.4999 0.4933 0.5003 0.4955 0.4996 0.5001 0.4923 0.5000 0.4998 0.5000 0.4986 0.4945 0.4990 0.4982 0.4933 0.4932 0.4967 0.4995 0.4978 0.4973 0.4975 0.5000 0.5003 0.4917 error_rms_valida = 0.0045 % Output of Test 1: Defect data D1, unknown for NN.. 0.4996 0.4993 0.4807 0.4873 0.5002 0.4990 0.5001 0.5004 0.5004 0.4989 0.4955 0.5002 0.4965 0.5003 0.4994 0.5002 0.4509 0.4727 0.4773 0.5005 0.4999 0.5003 0.5002 0.4873 0.5003 error_rms_def = 0.3492 % Output of Test 2: Defect data D2, unknown for NN.. 0.4999 0.4997 0.5007 0.4983 0.4991 0.4951 0.4980 0.4997 0.4995 0.4987 0.5001 0.4999 0.4966 0.4940 0.4360 0.4997 0.5002 0.4990 0.4662 0.4949 0.5002 0.4989 0.5004 0.5001 0.5000 erro_rms_def = 0.3500

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% Output of Test 2: Defect data D2, unknown for NN. 0.5002 0.4994 0.4949 0.5000 0.5002 0.5004 0.4997 0.4989 0.4996 0.5000 0.4998 0.5002 0.5000 0.4999 0.4480 0.5002 0.4981 0.4999 0.4980 0.4900 0.4982 0.4988 0.4924 0.4994 0.4988 error_rms_def = 0.3511 % Output of Test 4: Defect data D4, unknown for NN.. 0.5001 0.5004 0.4123 0.5003 0.5003 0.5000 0.4988 0.5001 0.4913 0.4983 0.5000 0.4859 0.4999 0.4480 0.4960 0.5002 0.5000 0.5004 0.4997 0.4981 0.5002 0.5001 0.5000 0.4997 0.4994 erro_rms_def = 0.3487

X. CONCLUSIONS

This paper has presented a sensorless method for fault detection in electrical generators using voltage as a variable to identify faults. A special facility was constructed in order to perform tests without and with failures introduced in the generator bearings.

It was observed that the harmonic decomposition of the vibration measurements carries a great amount of information, making it possible to create patterns. It was observed that the failures introduced in the generator bearing cause high frequencies in the harmonic decomposition of the measurements. The method used demonstrated how to identify vibration patterns in generators using voltage. A principal components analysis PCA was used for the reduction of information and the extraction of the main features in order to characterize possible patterns of the generator behavior. An artificial neural network using the results of the PCA as inputs and a backpropagation resilient training algorithm was able to identify the patterns. This method can be applied to most types of electric generators. Its low cost is other advantage of the control software of these generators.

ACKNOWLEDGMENT

The authors would like to express their gratitude for the financial and technical support provided by ELETRONORTE for the development of the MHACE research project and the institutional support of the Brazilian Government through the Coordination of Improvement of Personnel of Superior Level - CAPES.

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