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The Application of Information Theory in Fault Detection and Diagnosis

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 19869

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Dr. José de Jesús Rangel-Magdaleno

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Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico
Interests: FPGA; instrumentation; mechatronics; digital systems; fault detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, instrumentation plays an integral role within Industry 4.0. Regarding maintenance and condition monitoring, the state of the art demands the detection of faults or possible failures in the short term, requiring the development of signal processing and decision making algorithms.

The theory of instrumentation and power system engineering is linked with the general information theory and Shannon entropy. The proposed Special Issue will cover advanced research in instrumentation and signal processing for the detection, diagnosis, and classification of faults in power systems, transmission lines, induction machines, electromechanical systems, and power quality disturbances.

This Special Issue provides a forum for the presentation of new and improved techniques for signal processing applied to fault detection and classification in power systems and industrial machines based on information theory, entropy, and machine learning.

Dr. José de Jesús Rangel-Magdaleno
Guest Editor

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Keywords

fault diagnosis and prognosis
application of entropy in instrumentation and fault diagnosis
application of entropy in power systems for fault diagnosis
intelligent instrumentation
artificial intelligence and IoT in instrumentation
compressed sensing
early detection of incipient faults
signal processing for monitoring and diagnosis
information theory for patterns classification
multi-sensor information fusion for instrumentation and fault diagnosis
embedded systems for information theory processing
machine learning for fault detection and classification

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Published Papers (9 papers)

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Research

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16 pages, 1798 KiB

Open AccessArticle

Differential Entropy-Based Fault-Detection Mechanism for Power-Constrained Networked Control Systems

by Alejandro J. Rojas

Entropy 2024, 26(3), 259; https://doi.org/10.3390/e26030259 - 14 Mar 2024

Cited by 1 | Viewed by 1093

Abstract

In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach, we then discuss the finite-time approximation of the power constraints for the relevant control loop signals. The network under study is formed by two additive white Gaussian noise (AWGN) channels located on the direct and feedback paths of the closed control loop. The finite-time approximation of the controller output signal allows us to estimate its differential entropy, which is used in our proposed fault-detection mechanism. After fault detection, we propose a fault-identification mechanism that is capable of correctly discriminating faults. Finally, we discuss the extension of the contributions developed here to future research directions, such as fault recovery and control resilience. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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31 pages, 9698 KiB

Open AccessArticle

Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified Sparse Bayesian Classification Model

by Lixin Lu, Weihao Wang, Dongdong Kong, Junjiang Zhu and Dongxing Chen

Entropy 2023, 25(11), 1549; https://doi.org/10.3390/e25111549 - 16 Nov 2023

Cited by 3 | Viewed by 1248

Abstract

Fault diagnosis of rotating machinery plays an important role in modern industrial machines. In this paper, a modified sparse Bayesian classification model (i.e., Standard_SBC) is utilized to construct the fault diagnosis system of rotating machinery. The features are extracted and adopted as the input of the SBC-based fault diagnosis system, and the kernel neighborhood preserving embedding (KNPE) is proposed to fuse the features. The effectiveness of the fault diagnosis system of rotating machinery based on KNPE and Standard_SBC is validated by utilizing two case studies: rolling bearing fault diagnosis and rotating shaft fault diagnosis. Experimental results show that base on the proposed KNPE, the feature fusion method shows superior performance. The accuracy of case1 and case2 is improved from 93.96% to 99.92% and 98.67% to 99.64%, respectively. To further prove the superiority of the KNPE feature fusion method, the kernel principal component analysis (KPCA) and relevance vector machine (RVM) are utilized, respectively. This study lays the foundation for the feature fusion and fault diagnosis of rotating machinery. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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33 pages, 7932 KiB

Open AccessArticle

Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions

by Wei Cui, Jun Ding, Guoying Meng, Zhengyan Lv, Yahui Feng, Aiming Wang and Xingwei Wan

Entropy 2023, 25(8), 1233; https://doi.org/10.3390/e25081233 - 18 Aug 2023

Cited by 7 | Viewed by 1706

Abstract

Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings’ operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples’ feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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21 pages, 12420 KiB

Open AccessArticle

Analysis of Vibration Signals Based on Machine Learning for Crack Detection in a Low-Power Wind Turbine

by Angel H. Rangel-Rodriguez, David Granados-Lieberman, Juan P. Amezquita-Sanchez, Maximiliano Bueno-Lopez and Martin Valtierra-Rodriguez

Entropy 2023, 25(8), 1188; https://doi.org/10.3390/e25081188 - 9 Aug 2023

Cited by 3 | Viewed by 2094

Abstract

Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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12 pages, 4999 KiB

Open AccessArticle

Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest

by Li You, Zaixun Ling, Yibo Cui, Wanli Cai and Shunfan He

Entropy 2023, 25(5), 778; https://doi.org/10.3390/e25050778 - 10 May 2023

Cited by 1 | Viewed by 1717

Abstract

To detect open circuit faults of grid-connected T-type inverters, this paper proposed a real-time method based on fast S transform and random forest. The three-phase fault currents of the inverter were used as the inputs of the new method and no additional sensors were needed. Some fault current harmonics and direct current components were selected as the fault features. Then, fast S transform was used to extract the features of fault currents, and random forest was used to recognize the features and the fault type, as well as locate the faulted switches. The simulation and experiments showed that the new method could detect open-circuit faults with low computation complexity and the detection accuracy was 100%. The real-time and accurate open circuit fault detection method was proven effective for grid-connected T-type inverter monitoring. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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15 pages, 1050 KiB

Open AccessArticle

Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory

by Yongchuan Tang, Yonghao Zhou, Ying Zhou, Yubo Huang and Deyun Zhou

Entropy 2023, 25(5), 757; https://doi.org/10.3390/e25050757 - 6 May 2023

Cited by 3 | Viewed by 2845

Abstract

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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16 pages, 3382 KiB

Open AccessArticle

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

by David A. Elvira-Ortiz, Juan J. Saucedo-Dorantes, Roque A. Osornio-Rios and Rene de J. Romero-Troncoso

Entropy 2023, 25(3), 424; https://doi.org/10.3390/e25030424 - 26 Feb 2023

Cited by 6 | Viewed by 2206

Abstract

Gears are reliable and robust elements that are found in any power transmission system. However, gears are prone to present incipient faults, such as wear, since they are constantly subjected to contact forces. Due to gears playing a key role in many industrial processes, it is important to develop condition monitoring strategies that ensure the proper functioning of the related power transmission system and the overall components. In this regard, the data on entropy provide relevant information that allow us to identify and quantify the effect of different wear levels in gears. Therefore, in this work, we proposed the use of seven entropy-related features to perform the identification of different wear severities in a gearbox. The novelty of this proposal lies in the use of the entropy features to carry out a high-performance characterization of the available vibration signals that are acquired from experimental tests. The novelty of this proposal lies in the fusion of three different techniques: entropy features, linear discriminant analysis, and artificial neural networks to obtain a machine learning approach for improving the detection of different wear severities in gears compared to other reported methodologies. This situation is achieved due to the high-performance characterization of the available vibration signals that are acquired from experimental tests. Additionally, the entropy features are subjected to a feature space transformation by means of linear discriminant analysis to obtain a 2D representation and, finally, the set of features extracted by linear discriminant analysis are used as inputs of a neural network-based classifier to determine the severity of wear that is present in the gears. The proposed methodology is validated and compared with a conventional statistical approach to show the improvement in the classification. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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16 pages, 1815 KiB

Open AccessArticle

Broken Bar Fault Detection Using Taylor–Fourier Filters and Statistical Analysis

by Sarahi Aguayo-Tapia, Gerardo Avalos-Almazan, Jose de Jesus Rangel-Magdaleno and Mario R. A. Paternina

Entropy 2023, 25(1), 44; https://doi.org/10.3390/e25010044 - 27 Dec 2022

Cited by 4 | Viewed by 3217

Abstract

Broken rotor bars in induction motors make up one of the typical fault types that are challenging to detect. This type of damage can provoke adverse effects on the motors, such as mechanical and electrical stresses, together with an increase in electricity consumption, causing higher operative costs and losses related to the maintenance times or even the motor replacement if the damage has led to a complete failure. To prevent such situations, diverse signal processing algorithms have been applied to incipient fault detection, using different variables to analyze, such as vibrations, current, or flux. To counteract the broken rotor bar damage, this paper focuses on a motor current signal analysis for early broken bar detection and classification by using the digital Taylor–Fourier transform (DTFT), whose implementation allows fine filtering and amplitude estimation with the final purpose of achieving an incipient fault detection. The detection is based on an analysis of variance followed by a Tukey test of the estimated amplitude. The proposed methodology is implemented in Matlab using the O-splines of the DTFT to reduce the computational load compared with other methods. The analysis is focused on groups of 50-test of current signals corresponding to different damage levels for a motor operating at 50% and 75% of its full load. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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Review

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21 pages, 376 KiB

Open AccessReview

Entropy-Based Methods for Motor Fault Detection: A Review

by Sarahi Aguayo-Tapia, Gerardo Avalos-Almazan and Jose de Jesus Rangel-Magdaleno

Entropy 2024, 26(4), 299; https://doi.org/10.3390/e26040299 - 28 Mar 2024

Cited by 1 | Viewed by 1557

Abstract

In the signal analysis context, the entropy concept can characterize signal properties for detecting anomalies or non-representative behaviors in fiscal systems. In motor fault detection theory, entropy can measure disorder or uncertainty, aiding in detecting and classifying faults or abnormal operation conditions. This is especially relevant in industrial processes, where early motor fault detection can prevent progressive damage, operational interruptions, or potentially dangerous situations. The study of motor fault detection based on entropy theory holds significant academic relevance too, effectively bridging theoretical frameworks with industrial exigencies. As industrial sectors progress, applying entropy-based methodologies becomes indispensable for ensuring machinery integrity based on control and monitoring systems. This academic endeavor enhances the understanding of signal processing methodologies and accelerates progress in artificial intelligence and other modern knowledge areas. A wide variety of entropy-based methods have been employed for motor fault detection. This process involves assessing the complexity of measured signals from electrical motors, such as vibrations or stator currents, to form feature vectors. These vectors are then fed into artificial-intelligence-based classifiers to distinguish between healthy and faulty motor signals. This paper discusses some recent references to entropy methods and a summary of the most relevant results reported for fault detection over the last 10 years. Full article

(This article belongs to the Special Issue The Application of Information Theory in Fault Detection and Diagnosis)

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