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A. Głowacz and Z. Głowacz

Diagnostics of DC Machine Based on Analysis of Acoustic Signals with Application of MFCC and Classifier Based on Words

Technical diagnostics is concerned with the assessment of technical conditions of the machine through the study of properties of machine processes. Diagnostics is particularly important for factories and ironworks. In paper is presented method of diagnostics of imminent failure conditions of DC machine. This method is based on a study of acoustic signals generated by DC machine. System of sound recognition uses algorithms for data processing, such as Mel Frequency Cepstral Coefficient and classifier based on words. Software to recognize the sounds of DC machine was implemented on PC computer. Studies were carried out for sounds of faultless machine and machine with shorted coils. The results confirm that the system can be useful for diagnostics of dc and ac machines used in metallurgy.

Open access

A. Glowacz, A. Glowacz and Z. Glowacz

Abstract

Thermography is a technology that enables recognition of objects in the specific area. The goal of using thermographic techniques for ironworks is to diagnose electrical equipment. These techniques can be also use to increase safety and quality control in ironworks. Faulty equipment can be dangerous for engineers. Article describes the method of the recognition of imminent failure states of synchronous motor. Thermal images of the stator are used for an analysis of electrical machine. Researches of image processing techniques have been carried out for three states of motor. Proposed approach uses patterns recognition. Using of medial axis transformation and classifier based on words gave good results. In the future electrical machines and metallurgical equipment will use diagnostic systems based on recognition of thermal images.

Open access

A. Głowacz

Diagnostics of Induction Motor Based on Analysis of Acoustic Signals with the Application of Eigenvector Method and K-Nearest Neighbor Classifier

In this paper numerical experiments are proposed to investigate differences between the acoustic signals of induction motors. Four conditions of induction motor were considered. Investigations were carried out with application of eigenvector method and K-Nearest Neighbor classifier with Minkowski distance. Pattern creation process was conducted for 20 samples of sound. Identification process used 96 samples of sound. The obtained results confirm the correctness of the solutions methodology.

Open access

A. Glowacz, A. Glowacz and P. Korohoda

Abstract

This article discusses the recognition method of imminent failure conditions of synchronous motor. The proposed approach is based on a study of thermal images of the motor. Studies were carried out for four conditions of motor with the application of binarization and nearest mean classifier with Manhattan distance. Pattern creation process used 40 monochrome thermal images. Identification process was carried out for 160 monochrome thermal images. The experiments show that the method can be useful for protection of synchronous motor. Moreover, this method can be used to diagnose equipments in steelworks and other industrial plants.

Open access

A. Głowacz and Z. Głowacz

Abstract

Diagnosis of electrical direct current motors is essential for industrial plants. The emphasis is put on the development of diagnostic methods of solutions for capturing, processing and recognition of diagnostic signals. This paper presents a technique of early fault diagnosis of a DC motor. The proposed approach is based on acoustic signals. A real-world data of the DC motor were used in the analysis. The work provides an original feature extraction method called the shortened method of frequencies selection (SMoFS-15). The obtained results of the presented analysis show that the early fault diagnostic method can be used for monitoring electrical DC motors. The proposed method can also support other fault diagnosis methods based on thermal, current, and vibration signals.

Open access

A. Glowacz

Condition monitoring of deterioration in the metallurgical equipment is essential for faultless operation of the metallurgical processes. These processes use various metallurgical equipment, such as induction motors or industrial furnaces. These devices operate continuously. Correct diagnosis and early detection of incipient faults allow to avoid accidents and help reducing financial loss. This paper deals with monitoring of rotor electrical faults of induction motor. A technique of recognition of acoustic signals of induction motors is presented. Three states of induction motor were analyzed. Studies were carried out for methods of data processing: Method of Selection of Amplitudes of Frequencies (MSAF10) and Bayes classifier. Condition monitoring is helpful to protect induction motors and metallurgical equipment. Further researches will allow to analyze other metallurgical equipment.

Open access

A. Glowacz

Abstract

In this paper, a non-invasive method of early fault diagnostics of electric motors was proposed. This method uses acoustic signals generated by electric motors. Essential features were extracted from acoustic signals of motors. A plan of study of acoustic signals of electric motors was proposed. Researches were carried out for faultless induction motor, induction motor with one faulty rotor bar, induction motor with two faulty rotor bars and flawless Direct Current, and Direct Current motor with shorted rotor coils. Researches were carried out for methods of signal processing: log area ratio coefficients, Multiple signal classification, Nearest Neighbor classifier and the Bayes classifier. A pattern creation process was carried out using 40 samples of sound. In the identification process 130 five-second test samples were used. The proposed approach will also reduce the costs of maintenance and the number of faulty motors in the industry.

Open access

A. Glowacz, W. Glowacz, Z. Glowacz, J. Kozik, M. Gutten, D. Korenciak, Z. F. Khan, M. Irfan and E. Carletti

Abstract

A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies – Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

Open access

Anna Zwierzchowska, Ewa Sadowska-Krępa, Marta Głowacz, Aleksandara Mostowik and Adam Maszczyk

Abstract

The objectives of the present study were twofold: to determine differences between groups by means of chosen coefficients and to create significant predictors using regression models for athletes in wheelchair rugby who had the same spinal cord injury (tetraplegia) and were classified as low point and high point players. The study sample consisted of 24 subjects, who had sustained cervical spinal cord injury (CSCI). They were divided into low point (n=15) and high point (n=9) groups according to the IWRF Classification System. A one-way ANOVA revealed statistically significant differences in the following coefficients differentiating the groups: AC (η2=0.778), LC (η2=0.687), IC (η2=0.565), SC (η2=0.580). The Tukey’s HSD post-hoc test indicated statistically significant higher values of coefficients in the HP compared to the LP group: AC=0.958 (p=0.022), LC=0.989 (p=0.031), IC=0.971 (p=0.044), SC=0.938 (p=0.039). In the HP group, the most significant predictor was the sum of visceral and trunk fat which was negatively correlated with the SC (what constituted a positive adaptive change in response to training). With regard to the LP group, body height and circumference of the chest appeared to be most significant predictors and were positively correlated with the SC. In the LP group no predictor with respect to the SC was significantly correlated to sports training. Therefore, the functional classification system confirmed lower status of the LP players. The results of the present study indicate that both metabolic and somatic profiles which highly determine potential of wheelchair rugby athletes are significantly different in LP and HP players, what confirms the reliability of the functional classification system.

Open access

Anna Zwierzchowska, Marta Głowacz, Agnieszka Batko-Szwaczka, Joanna Dudziňska-Griszek, Aleksandra Mostowik, Miłosz Drozd and Jan Szewieczek

Abstract

The enforced sedentary lifestyle and muscle paresis below the level of injury are associated with adipose tissue accumulation in the trunk. The value of anthropometric indicators of obesity in patients with spinal cord injuries has also been called into question. We hypothesized that the Body Mass Index recommended by the WHO to diagnose obesity in general population has too low sensitivity in case of wheelchair rugby players.

The study group comprised 14 wheelchair rugby players, aged 32.6 ± 5.1 years, who had sustained CSCI (paralysis of lower limbs and upper extremities). The research tool was the Tanita Viscan visceral and trunk fat analyzer AB140 using the abdominal bioelectrical impedance analysis (BIA) to estimate the visceral fat level (Vfat) and trunk fat percentage (Tfat). The AB140 analyzer also allowed the measurement of body composition of those individuals who could not assume an upright position. Our analyses revealed high and very high correlation coefficients between Vfat and WC (r=0.9), WHtR (r=0.7) and Tfat (r=0.9) whereas the correlation between Vfat and the BMI was weak, especially in the subgroup with Vfat < 13.5% ( r=0.2). The subgroup with Vfat>13.5 exhibited a moderate-level relationship between the BMI and visceral fat increase. It was concluded that the BMI had a low sensitivity for predicting obesity risk in wheelchair rugby players after CSCI. The sensitivity of WC measurement was higher and thus, it may be stated that it constitutes an objective tool for predicting obesity risk in post-CSCI wheelchair rugby players.