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Data-driven fault diagnosis approaches for industrial equipment: A review

EXPERT SYSTEMS 2024年第2期41卷 e13360-e13360页

作者： Sahu, Atma Ram Palei, Sanjay Kumar Mishra, Aishwarya Godavari Inst Engn & Technol A Dept Min Engn Rajamahendravaram Andhra Pradesh India Indian Inst Technol BHU Dept Min Engn Varanasi 221005 Uttar Pradesh India

Undetected and unpredicted faults in heavy industrial machines/equipment can lead to unwanted failures. Therefore, prediction of faults puts paramount importance on maintaining the reliability and availability of capital-intensive equipment. Due to large number of interconnected and interdependent mechanical and electrical components in the machines, fault analysis becomes a complex and challenging task. Under these circumstances, data-driven fault diagnosis (DDFD) is one of the most powerful, reliable and cost-effective artificial intelligence tools to detect, isolate, identify and classify the occurrence of faults. This article aims to make a comprehensive literature survey of various DDFD approaches used for analysing faults in industrial machines/equipment;and summarizing the strengths, limitations, and possible applications of existing fault diagnosis methods. Analysing and synthesizing 190 research works conducted on DDFD in last two decades revealed three types of DDFD approaches: supervised-learning, semi-supervised-learning and unsupervised-learning-based fault diagnosis. The supervised-learning is predominantly applied for fault diagnosis contributing to 82% of research works. Therefore, this article puts special emphasis on two supervised-learning-based approaches for fault diagnosis: (i) classification-based artificial neural network approach, and (ii) inference-based Bayesian network approach. Finally, these fault diagnosis approaches have been briefly discussed with effectiveness of the models and their possible inclusion in future industrial applications.

关键词： artificial intelligence data-driven approach fault diagnosis industrial equipment machine learning algorithm

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Assessing the impact of anthropogenic activities on land use and land cover changes in the semi-arid and arid regions of Algeria

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第4期196卷 383-383页

作者： Mohammed, Ghefar Boutkhil, Morsli Ayoub, Bouazzaoui Natl Inst Forestry Res INRF Sidi Bel Abbes 07 Sidi Bel Abbes 22000 Algeria Natl Inst Forestry Res INRF Tilimsen 13000 Algeria Univ Tlemcen Dept Forest Resources Res Lab N 31 Conservatory Management Water Soil & Forests Tilimsen 13000 Algeria

Land use and land cover are critical factors that influence the environment and human societies. The dynamics of LULC have been constantly changing over the years, and these changes can be analyzed at different spatial and temporal scales to evaluate their impact on the natural environment. This study employs multitemporal satellite data to investigate the spatial and temporal transformations that occurred in Sidi Bel Abbes province, situated in the northwestern region of Algeria, spanning from the early 1990s to 2020. Notably, this province is marked by semi-arid and arid climates and hosts a wide range of areas susceptible to gravitational hazards, especially concerning alterations in land use and forest fires. The interactive supervised classification tool utilized multiple machine learning algorithms including Random Forest, Support Vector machine, Classification and Regression Tree, and Naive Bayes to produce land cover maps with six main classes: forest, shrub, agricultural, pasture, water, and built-up. The findings showed that the LULC in the research area is undergoing continuous change, particularly in the forest and agricultural lands. The forest area has decreased significantly from 10.80% in 1990 to 5.25% in 2020, mainly due to repeated fires. Agricultural land has also undergone fluctuations, with a decrease between 1990 and 2000, followed by a fast increase and near stabilization in 2020. At the same time, pasture lands and built-up areas grew steadily, increasing by 11% and 13% respectively. This research highlights the significant impact of anthropogenic activities on LULC changes in the study area and can provide valuable insights for promoting sustainable land use policies.

关键词： LULC Change detection Anthropogenic Landsat machine learning algorithm

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Epilepsy Diagnosis Analysis via a Multiple-Measures Composite Strategy from the Viewpoint of Associated Network Analysis Methods

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3015-3015页

作者： Niu, Haoying Mu, Tiange Wang, Yuting Huang, Jiayang Liu, Jie Wuhan Text Univ Res Ctr Nonlinear Sci Wuhan 430073 Peoples R China Wuhan Text Univ Sch Math & Phys Sci Wuhan 430073 Peoples R China

Based on some typical complex network analysis methods and machine learning techniques, a general multiple-measures composited strategy-guided epilepsy diagnosis analysis framework is proposed in this brief paper. Five typical network analysis methods for biology time series analysis are utilized for real applications, including the classical visibility graph (VG), horizontal visibility graph (HVG), the limited penetrable visibility graph (LPVG), the modified frequency degree method (MFDM), and the quantity graph (QG). By using the aforementioned typical transformation methods, the EEG signal sets to be classified are transferred into graph network object sets. The main network features and related indicators are calculated and extracted as features for classification tasks. Some key features are selected via variance analysis, and the eXtreme Gradient Boosting (XGBOOST) machine learning algorithm is used for related binary and five-class classification tasks for electroencephalographic time series. Numerical experiments demonstrate that, through ten-fold cross-validation on the entire dataset, the classification accuracy for two-class classification consistently reaches 97.8% (with a specificity of 97.5%), while for five-class classification, the accuracy stably reaches 82.4% (with a specificity of 95.6%). Therefore, our classification framework can be effectively used to assist hospital doctors and medical specialists in diagnosing related diseases, especially to help accelerate the treatment of epilepsy patients.

关键词： electroencephalographic time series network analysis method complex networks machine learning algorithm

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Few-shot learning based multi-weather-condition impedance identification for MPPT-controlled PV converters

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IET RENEWABLE POWER GENERATION 2022年第7期16卷 1345-1353页

作者： Peng, Yang Wang, Yue Liu, Yonghui Gao, Kaijie Yin, Taiyuan Yu, Hanqiao Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt Xian Peoples R China

The broadband impedance of converters is an essential feature for the stability analysis of new energy sources. However, obtaining the impedance for photovoltaic (PV) converters with Maximum Power Point Tracking (MPPT) control is challenging because of their non-linear control schemes and real-time changing operating points. In this case, conventional linearized impedance modelling methods are not applicable, and conventional direct measurement approaches are highly time-consuming. This paper proposes a few-shot learning approach for quick access to PV converters' impedance. The proposed method is based on the model agnostic meta-learning (MAML) algorithm, suitable for MPPT-controlled converters whose impedance changes with time, temperature, and irradiation. In the training process, it adjusts the initial model of the machine learning algorithm under different weather conditions. After completing the training, the initial model can adapt to a new condition with very few samples. Under this approach, with only a few data measured at several frequency points, broadband impedance for MPPT-controlled converters under any weather conditions can be accurately predicted, avoiding time-consuming measurements and inaccurate prediction of existing methods. Contrast simulation results show the effectiveness and superiority of the proposed method.

关键词： MPPT-controlled converters photovoltaic power systems frequency points power generation control maximum power point trackers learning (artificial intelligence) conventional direct measurement approaches real-time changing operating points model agnostic meta-learning algorithm MPPT-controlled PV converters conventional linearized impedance modelling methods highly time-consuming broadband impedance Maximum Power Point Tracking control multiweather-condition impedance identification time-consuming measurements few-shot learning approach nonlinear control schemes photovoltaic converters machine learning algorithm

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Predictive Analysis and Prognostic Approach of Diabetes Prediction with machine learning Techniques

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期127卷 465-478页

作者： Omana, J. Moorthi, M. Anna Univ Prathyusha Engn Coll Dept Comp Sci & Engn Thiruvallur India Saveetha Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India

Medical experts indulge in numerous strategies for efficient and predictive measures to model the health status of patients and formulate the patterns that are formed in test results. Most patients would dream of their betterments of their health conditions and thus preventing the progression of any disease. When diabetics is considered in the model, or highly intervening methodology would be required for pre-diabetic individuals. Hidden Markov models have been modified into variant models to derive predictions that accurately produce expected results by investigating patterns of clinical observations from a detailed sample of patient's dataset. There are yet unanswered and concerning challenges to derive an absolute model for predicting diabetes. The datasets from which the patterns are derived from, still holds levels of in completeness, irregularity and obvious clinical interventions during the diagnosis. The Electronic Medical Records are not furnished with all requisite information in all conditions and scenarios. Due to these irregularities prediction has become highly challenging and there is increase in misclassification rate. Newton's Divide Difference Method (NDDM) is a conventional model for filling the irregularity in electronic datasets through divided differences. The classical approach considers a polynomial approximation approach, thus leading to Runge Phenomenon. If the interval between data fields id higher, severity of finding the irregularities is even higher. By using this type of technique it helps in improving the accuracy thereby bringing in high level prediction without any error and misclassification. In this technique proposed, a novel approximation technique is implemented using the Euclidean distance parameter over the NDDM approximation to predict the outcomes or risk of Type 2 Diabetes Mellitus among patients. Real world entities in CPCSSN are considered for this study and proposed method is tested. The proposed method filled the irreg

关键词： Prognostic modelling Prediction Automated modelling Type 2 diabetes mellitus Sparse data handling Approximation machine learning algorithm

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A comprehensive investigation on application of machine learning for optimization of process parameters of laser powder bed fusion-processed 316L stainless steel

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第7-8期123卷 2733-2756页

作者： Eshkabilov, Sulaymon Ara, Ismat Azarmi, Fardad North Dakota State Univ Dept Ag & Biosyst Engn 105 ABENNDSU Dept 7620 Fargo ND 58108 USA North Dakota State Univ Dept Mech Engn Fargo ND 58105 USA

Metal 3D printing has gained a lot of attention among industries since it offers a practical solution to problems rising during the manufacturing of parts and components with complex geometry. This is an additive technology that eliminated several fabrication steps and at the same time reduces material waste during the manufacturing process. However, in all additive manufacturing technologies, the final properties of the parts are determined by the operational process parameters. In this study, several machine learning algorithms were examined to characterize the effects of the printing process parameters on relative density, hardness, yield strength, and tensile strength in manufactured parts. It was possible by using "Big Data" collected from a large number of previously published articles on the application of laser powder bed fusion (LPBF) for the 3D printing of 316L stainless steel samples. Among different process parameters, laser power, laser energy density, and scanning speed were proven to have the largest effects directly on the physical and mechanical properties of LPBF-processed parts. Six different classification models and five support vector machine regression-based models were tested to find the most accurate prediction algorithm. To validate the obtained results from the applied machine learning models, a set of 316L specimens were produced using LPBF technology using a random set of process parameters. The physical and mechanical properties of 3D printed samples were tested and compared to the ones predicted from the optimum models from machine learning analysis. The results were in great agreement, which shows the high accuracy of the developed machine learning algorithms in this study.

关键词： Process parameter optimization Laser powder bed fusion machine learning algorithm Mechanical properties Microstructural characterization

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A machine learning approach to mapping canopy gaps in an indigenous tropical submontane forest using WorldView-3 multispectral satellite imagery

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ENVIRONMENTAL CONSERVATION 2022年第4期49卷 255-262页

作者： Jackson, Colbert M. Adam, Elhadi Univ Witwatersrand Sch Geog Archaeol & Environm Studies ZA-2050 Johannesburg South Africa

Selective logging in tropical forests may lead to deforestation and forest degradation, so accurate mapping of it will assist in forest restoration, among other ecological applications. This study aimed to track canopy tree loss due to illegal logging of the important hardwood tree Ocotea usambarensis in a closed-canopy submontane tropical forest by evaluating the mapping potential of the very-high-resolution WorldView-3 multispectral dataset using random forest (RF) and support vector machine (SVM) with radial basis function kernel classifiers. The results show average overall accuracies of 92.3 +/- 2.6% and 94.0 +/- 2.1% for the RF and SVM models, respectively. Average kappa coefficients were 0.88 +/- 0.03 for RF and 0.90 +/- 0.02 for SVM. The user's and producer's accuracies for both classifiers were in the range of 84-100%. This study further indicates that vegetation indices derived from bands 5 and 6 helped detect canopy gaps in the study area. Both variable importance measurement in the RF algorithm and pairwise feature selection proved useful in identifying the most pertinent variables in the classification of canopy gaps. These findings could allow forest managers to improve methods of detecting canopy gaps at larger scales using remote sensing data and relatively little additional fieldwork.

关键词： canopy gaps machine learning algorithm Ocotea usambarensis selective logging tropical forests WorldView-3

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Bearing fault diagnostic using machine learning algorithms

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2020年第4期9卷 341-350页

作者： Sawaqed, Laith S. Alrayes, Ayman M. Jordan Univ Sci & Technol Univ Mech Engn Dept Irbid Jordan

This study aims to enhance the condition monitoring of external ball bearings using the raw data provided by Paderborn University which provided sufficient data for motor current signal MCS. Three classes of bearings have been used: healthy bearings, bearings with an inner race defect, and bearings with outer race defect. Online data at different operating conditions, bearings, and faults extent of artificial and real damages have been chosen to provide the generalization and robustness of the model. After proper preprocessing to the raw data of vibration and MCS, time, frequency, and time-frequency domain features have been extracted. Then, optimal features have been selected using genetic algorithm. Artificial neural network with optimized structure using genetic algorithm has been implemented. A comparison between the performance of vibration and motor current signal has been presented. Moreover, our results are compared to previous work by using the same raw data. Results showed the potential of motor current signal in bearing fault diagnosis with high classification accuracy. Moreover, the results showed the possibility to provide a promised diagnostic model that can diagnose bearings of real faults with different fault severities using MCS.

关键词： Bearing damage detection machine fault diagnostic Vibration Motor current signal machine learning algorithm Neural networks Genetic algorithm

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Optical Sensor for Water Bacteria Detection using machine learning 18

Optical Sensor for Water Bacteria Detection using Machine Le...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chirchi, Vanajaroselin Chirchi, Emmanvelraj Khushi, E.C. Bairavi, S.M. Indu, K.S. The Oxford College of Engineering Dept. of Ise Bangalore India Don Bosco Institute of Technology Dept. of Ise Bengaluru India Dayanadasagar University Dept. of Cse Bengaluru India

ISBN: (纸本)9789380544519

The proposed method focuses on the detection of Escherichia coli bacterial contamination in water supplies. The proposed method uses a photonic-based sensor to examine photonic crystals in order to identify bacterial contamination in water. The system accurately predicts and diagnoses water contamination caused by bacteria by integrating an optical sensor with a graphical user interface (GUI). By attentively scrutinizing the early sensor-generated output graphs, we are able to discover variations in drainage patterns that are signaling the presence of bacteria in water samples. These graphs offer crucial insights into the unique characteristics of many samples, such as their refractive index. The sensor's sensitivity, specificity, and accuracy have all been thoroughly evaluated, and its results have been compared to those of established reference methods. A 95% accuracy rate is attained in the effective detection of the bacterium. These comparisons demonstrate the effectiveness of our AI-driven model and its intuitive graphical user interface (GUI), while also highlighting the potential of our machine learning techniques, such as Decision tree and Naive Bayes algorithm, for effective water bacterial detection. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Accuracy Bacteria Decision Tree machine learning algorithm Naïve Bayes algorithm Sensitivity Sensor

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Exploring machine learning Techniques for Music Emotion Classification: A Comprehensive Review 18

Exploring Machine Learning Techniques for Music Emotion Clas...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Shelke, Sheetal Patil, Mangal College of Engineering Electronics Engineering Pune India

ISBN: (纸本)9789380544519

Music Emotion Recognition (MER) has made substantial advancements in recent years, driven by improvements in signal processing, machine learning, and interdisciplinary collaborations. The work analyzes the most recent advancements in MER, with a specific emphasis on fundamental methodologies, obstacles, and emerging patterns. The review commences by clarifying the fundamental theories of music and emotion, emphasizing the complex interaction between auditory stimuli and emotional reactions. The text examines the progression of feature extraction methods from conventional acoustic features to deep learning representations, offering a comprehensive insight into the techniques used to capture the subtle emotional nuances present in music signals. The paper thoroughly examines the various machine learning models used in MER, emphasizing their advantages and drawbacks. Further, the review delves into the accessibility and variety of datasets, recognizing their crucial importance in training resilient models for emotion recognition. At last, it provides a thorough analysis of the current state of Music Emotion Recognition, with the goal of assisting researchers, practitioners, and enthusiasts in exploring the ever-changing field. It also aims to encourage further progress in comprehending the deep relationship between music and human emotions. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： machine learning algorithm Music Emotion Classification Music Files

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