Self-adaptive fault diagnosis of roller bearings using infrared thermal images

Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new self-adaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy are applied respectively to decompose images and seek for the desired decomposition level of the approximation coefficients. After that, the histograms of selected coefficients are used as an input of the feature space selection method by using Genetic Algorithm (GA) and Nearest Neighbor (NN), for the purpose of selecting two salient features that can achieve the highest classification accuracy. The results have demonstrated that the proposed scheme can be employed effectively as an intelligent system for bearing fault diagnosis in rotating machinery

Evaluating the Thermal Condition of Electrical Equipment Via IRT Image Analysis

The integrity of electrical power equipment is of paramount importance when itsupplies electricity throughout a facility. However, the reliability of the equipments will degraded after sometime, and appropriate maintenance has to be taken accordingly to avoid future faults. Infrared thermography (IRT) image analysis is a commonly used technique for diagnosing the reliability of electrical equipments. Conventionally, the analysis of infrared image is done manually and takes very long time for further analysis. This paper proposes an automatic thermal fault detection and classification system for evaluating thecondition of electrical equipment by analyzing its infrared image. First, the image is segmented to find the target region of interest (ROI). The detected regions which have the same region properties are grouped together in order to remove the unwanted regions. Finally, statistical features from each detected region are extracted and classified using the support vector machine (SVM) algorithm. The thermal condition of electrical equipments is evaluated based on qualitative measurement technique. The experimental result shows that the proposed system can detect and classify the thermal condition of electrical equipments

Thermal imaging and vibration-based multisensor fault detection for rotating machinery

In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early and automatically. To enable this, sensor streams should continuously be monitored, processed, and interpreted. In recent years, infrared thermal imaging has gained attention for the said purpose. However, the detection capabilities of a system that uses infrared thermal imaging is limited by the modality captured by this single sensor, as is any single sensor-based system. Hence, within this paper a multisensor system is proposed that not only uses infrared thermal imaging data, but also vibration measurements for automatic condition and fault detection in rotating machinery. It is shown that by combining these two types of sensor data, several conditions/faults and combinations can be detected more accurately than when considering the sensor streams individually

Application of infrared thermography to failure detection in industrial induction motors: case stories

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] Infrared thermography has been extensively applied over decades to areas such as maintenance of electrical installations. Its use in electrical machinery has been mainly circumscribed to the detection of faults in static machines, such as power transformers. However, with regard to the predictive maintenance of rotating electrical machines, its use has been much more limited. In spite of this fact, the potential of this tool, together with the progressive decrease in the price of infrared cameras, make this technique a very interesting option to, at least, complement, the diagnosis provided by other well-known techniques, such as current or vibration data analysis. In this context, infrared thermography has recently shown potential for the detection of motor failures including misalignments, cooling problems, bearing damages or connection defects. This work presents several industrial cases that help to illustrate the effectiveness of this technique for the detection of a wide range of faults in field induction motors. The data obtained with this technique made it possible to detect the presence of faults of diverse nature (electrical, mechanical, thermal and environmental); these data were very useful to either diagnose or to complement the diagnosis provided by other tools.This work was supported in part by the Spanish Ministerio de Economia y Competitividad and the FEDER Program in the framework of the Proyectos I+D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia under Grant DPI2014-52842-P.Lopez-Perez, D.; Antonino-Daviu, J. (2017). Application of infrared thermography to failure detection in industrial induction motors: case stories. IEEE Transactions on Industry Applications. 53(3):1901-1908. https://doi.org/10.1109/TIA.2017.2655008S1901190853

Combination of Noninvasive Approaches for General Assessment of Induction Motors

[EN] There exists no single quantity able to diagnose all possible failures taking place in induction motors. Currents and vibrations monitoring are rather common in the industry, but each of these quantities alone can only detect some specific failures. Moreover, even for the specific faults that a quantity is supposed to detect, many problems may rise. As a consequence, a reliable and general diagnosis system cannot rely on a single quantity. On the other hand, it would be desirable to rely on quantities that can be measured in a noninvasive way, which is a crucial requirement in many industrial applications. This paper proposes a twofold method to detect electromechanical failures in induction motors. The method relies on analysis of currents (steady state + transient) combined with analysis of infrared data captured by using appropriate cameras. Each of these noninvasive techniques may provide complementary information that may be very useful to diagnose an enough wide range of failures. In the present paper, the detection of three illustrative faults is analyzed: broken rotor bars, cooling system problems and bearing failures. The results show the potential of the methodology that may be particularly suitable for large, expensive motors, where the prevention of eventual failures justifies the costs of such system, due to the catastrophic implications that these unexpected faults may have.Picazo-Rodenas, MJ.; Antonino-Daviu, J.; Climente Alarcon, V.; Royo, R.; Mota-Villar, A. (2015). Combination of Noninvasive Approaches for General Assessment of Induction Motors. IEEE Transactions on Industry Applications. 51(3):2172-2180. doi:10.1109/TIA.2014.2382880S2172218051

Evaluating the Thermal Condition of Electrical Equipment via IRT Image Analysis

The integrity of electrical power equipment is of paramount importance when itsupplies electricity throughout a facility. However, the reliability of the equipments will degraded after sometime, and appropriate maintenance has to be taken accordingly to avoid future faults. Infrared thermography (IRT) image analysis is a commonly used technique for diagnosing the reliability of electrical equipments. Conventionally, the analysis of infrared image is done manually and takes very long time for further analysis. This paper proposes an automatic thermal fault detection and classification system for evaluating thecondition of electrical equipment by analyzing its infrared image. First, the image is segmented to find the target region of interest (ROI). The detected regions which have the same region properties are grouped together in order to remove the unwanted regions. Finally, statistical features from each detected region are extracted and classified using the support vector machine (SVM) algorithm. The thermal condition of electrical equipments is evaluated based on qualitative measurement technique. The experimental result shows that the proposed system can detect and classify the thermal condition of electrical equipments

A Thermal Image based Fault Detection in Electric Vehicle Battery Cells Utilizing CNN U-Net Model

It entails the formation of thermal images from battery cells under different conditions, capturing crucial thermal patterns such as hotspots, insulation degradation, and overheating. For robust model training, data preprocessing and augmentation techniques are applied. The U-Net model, known for its expertise in semantic segmentation tasks, is applied to evaluate thermal images and to detect fault-related features. The results demonstrate the U-Net's unique precision, sensitivity, and specificity in detecting thermal anomalies. This research adds to the improvement of the safety and dependability of EV battery systems, with applications in the electric mobility and automotive industries

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section