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Fault diagnosis in reciprocating air compressors using ensemble voting classifier

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Reddy, Suhash Sridharan, Naveen Venkatesh Sivakumar, Aravinth Vaithiyanathan, Sugumaran Vellore Inst Technol Sch Mech Engn SMEC Vandalur Kelambakkam Rd Chennai 600127 India Lulea Univ Technol Div Operat & Maintenance Engn Lulea Sweden Sri Ranganathar Inst Engn & Technol Deparment Mech Engn Coimbatore India

Reciprocating air compressors play a pivotal role in various industrial applications, demanding a steadfast focus on their dependable operation. This research addresses the challenge of fault diagnosis in reciprocating air compressors by leveraging the potential of ensemble voting classifiers. Vibration signals were captured under five compressor conditions, covering both normal operation and multiple fault scenarios, such as inlet valve fluttering, outlet valve fluttering, check valve faults, and combined inlet and outlet valve issues. A range of features, including statistical and autoregressive moving average features were extracted from the acquired vibration signals to effectively differentiate among these conditions. Feature selection was executed using the J48 algorithm to identify the most pertinent indicators. During the classification phase, an exhaustive evaluation of individual classifiers, such as K-Nearest Neighbors, J48, support vector machine, multilayer perceptron (MLP), Na & iuml;ve Bayes, logistic regression, random forest (RF), and logistic model tree, was conducted. The top-performing classifiers were subsequently selected for incorporation into ensemble voting systems. Within the ensemble voting framework, various configurations were explored, ranging from two to five classifiers, employing multiple voting strategies to optimize fault diagnosis performance. The findings of this study showcase that the combination of MLP and RF classifiers for statistical features produced the maximum classification accuracy of 98.67%.

关键词： Air compressors fault diagnosis voting algorithm J48 algorithm valve fault

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TAMING voting algorithmS ON GPUS FOR AN EFFICIENT CONNECTED COMPONENT ANALYSIS algorithm

TAMING VOTING ALGORITHMS ON GPUS FOR AN EFFICIENT CONNECTED ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lemaitre, Florian Hennequin, Arthur Lacassagne, Lionel Sorbonne Univ CNRS LIP6 Paris France CERN LHCb Expt Geneva Switzerland

ISBN: (纸本)9781728176055

Connected Component Analysis is vastly used as a building block for many Computer Vision algorithms from many fields like medical image processing, surveillance, or autonomous driving. It extends Connected Component Labeling by computing some features of the connected components like their bounding box or their surface. As such, Connected Component Analysis is a voting algorithm just like histogram computation or Hough transform. voting algorithms are difficult on many-core architectures like GPUs because of the serialization of atomic memory accesses. The trend to increase the number of cores makes this issue even more critical. This paper explores multiple ways to reduce those conflicts for voting algorithms and especially for Connected Component Analysis. We show that our new algorithm is from 4 up to 10 times faster than State-of-the-Art on average on an Nvidia A100.

关键词： voting algorithm Connected Component Analysis GPU Cuda Histogram

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New fault-tolerant control architectures based on voting algorithms for electric vehicle induction motor drive

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2016年第9期38卷 1120-1135页

作者： Raisemche, A. Boukhnifer, M. Diallo, D. GEEPS Grp Elect Engn Paris 11 Rue Joliot Curie Plateau de Moulon F-91192 Gif Sur Yvette France ESTACA Ecole Ingenieurs Paris 34 Rue Victor Hugo F-92300 Levallois Perret France

In this paper, we present a comparative study of four voting algorithms in two observer-based fault-tolerant control (FTC) architectures for an electric vehicle (EV) induction motor drive. The first architecture, called output FTC, is based on the mechanical sensor, an EKF and a second-order sliding mode observer (SMO2). The second one, input FTC, is based on three controllers (PI, H loop shaping and the generalized internal model control), the most appropriate being selected to ensure good behaviour in presence of a multiplicative sensor fault (the fault is modelled as an exponential type emulating a bias). A third architecture, called hybrid FTC, based on the previous output and input fault-tolerant schemes, is built to mitigate simultaneous faults. Simulation and experimental results for a 7.5-kW induction motor drive show the efficiency of the approaches and their robustness against parametric variations for different load conditions.

关键词： Fault-tolerant control GIMC H loop shaping induction motor Kalman filter second-order sliding mode observer voting algorithm

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Soft Inference as a voting Mechanism in k-Nearest Neighbors Clustering algorithm 29th

Soft Inference as a Voting Mechanism in k-Nearest Neighbors ...

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29th International Conference on Information and Software Technologies (ICIST)

作者： Bury, Tomasz Kacprzak, Aleksandra Zerdzinski, Piotr Silesian Tech Univ Fac Appl Math Kaszubska 23 Gliwice Poland

ISBN: (纸本)9783031489808;9783031489815

The rapid growth of IT systems that use artificial intelligence algorithms necessitates increasingly accurate methods. To handle data uncertainty, computer scientists can employ soft sets. One popular classification method in machine learning that utilizes the idea of proximity between data points is the k-NN algorithm. In this paper, we describe a modification to the k-NN algorithm that makes use of soft sets to take into account uncertainty in the classification process. This is achieved by introducing soft inference as a voting mechanism. The authors present a mathematical model with pseudocode for re-implementation purposes and demonstrate and discuss experimental results from conducted tests to show the effectiveness of the proposed approach.

关键词： k-Nearest Neighbor (k-NN) soft sets voting algorithm

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Fault Tolerant Control to Mechanical Sensor Failures for Induction Motor Drive: A Comparative Study of voting algorithms

Fault Tolerant Control to Mechanical Sensor Failures for Ind...

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39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Boukhnifer, Moussa Raisemche, Aziz Diallo, Demba Larouci, Cherif ESTACA Lab Commande & Syst 34 Rue Victor Hugo F-92300 Levallois Perret France Univ Paris Sud CNRS Lab Genie Electrique Paris UMR 8507 F-91192 Gif Sur Yvette France

ISBN: (纸本)9781479902248

In this paper, we present a comparative study of four voting algorithms for two Induction Motor drive fault tolerant control to speed sensor failure schemes. In both Output and Input Fault Tolerant controller, the voting algorithm chooses the most appropriate output signal to ensure the best behavior in degraded mode. The performances are evaluated through simulations of a 7.5kW Induction Motor drive with robustness testing against parametric variations, as well as under load testing. The results show that Euler, Newton-Raphson and Maximum Likelihood voting algorithms are more efficient than the Weighted Average in both Fault Tolerant Control schemes.

关键词： Induction motor voting algorithm Kalman Filter 2nd order sliding mode observer Fault Tolerant Control

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Bioluminescent Proteins Prediction with voting Strategy

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CURRENT BIOINFORMATICS 2021年第2期16卷 240-251页

作者： Zhao, Shulin Ju, Ying Ye, Xiucai Zhang, Jun Han, Shuguang Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Univ Tsukuba Dept Comp Sci Tsukuba Japan Heilongjiang Prov Land Reclamat Headquarters Gen Rehabil Dept Harbin Peoples R China Univ Elect Sci & Technol China Ctr Informat Biol Chengdu Peoples R China

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology. In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Methods: In this study, a new bioluminescent proteins prediction method, based on a voting algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were used. 314 dimensional features in total were extracted from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, a voting algorithm was then used to build the *** create the best performing model, the selection of base classifiers and vote counting rules are discussed. Results: The proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. a previous prediction of bioluminescent proteins in three lineages was also improved using the model building method, resulting in greatly

关键词： Bioluminescent proteins prediction feature extraction voting algorithm base classifiers vote counting rules

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Fault Tolerant Control to Mechanical Sensor Failures for Induction Motor Drive: A Comparative Study of voting algorithms

Fault Tolerant Control to Mechanical Sensor Failures for Ind...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Moussa Boukhnifer Aziz Raisemche Demba Diallo Cherif Larouci Laboratoire Commande et Systemes ESTACA Laboratoire de Genie Electrique de Paris Univ. Paris Sud

ISBN: (纸本)9781479902255

关键词： Induction motor voting algorithm Kalman Filter 2nd order sliding mode observer Fault Tolerant Control fault-tolerant control induction motors induction motor drives Vote Mechanical sensors Kalman filters

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Simultaneous detection of multiple debris via a cascade of numerical evaluations and a voting scheme for lines in an image sequence

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ACTA ASTRONAUTICA 2017年 133卷 416-422页

作者： Fujita, Koki Ichimura, Naoyuki Hanada, Toshiya Kyushu Univ Dept Aeronaut & Astronaut Nishi Ku 744 Motooka Fukuoka 8190395 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Cent 21-1-1 Tsukuba Ibaraki 3058568 Japan

This paper presents a novel method to simultaneously detect multiple trajectories of space debris in an observation image sequence to establish a reliable model for space debris environment in Geosynchronous Earth Orbit (GEO). The debris in GEO often appear faintly in image sequences due to the high altitude. A simple but steady way to detect such faint debris is to decrease a threshold value of binarization applied to an image sequence during preprocessing. However, a low threshold value of binarization leads to extracting a large number of objects other than debris that become obstacles to detect debris trajectories. In order to detect debris from binarized image frames with massive obstacles, this work proposes a method that utilizes a cascade of numerical evaluations and a voting scheme to evaluate characteristics of the line segments obtained by connecting two image objects in different image frames, which are the candidates of debris trajectories. In the proposed method, the line segments corresponding to objects other than debris are filtered out using three types of characteristics, namely displacement, direction, and continuity. First, the displacement and direction of debris motion are evaluated to remove irrelevant trajectories. Then, the continuity of the remaining line segments is checked to find debris by counting the number of image objects appearing on or close to the line segments. Since checking the continuity can be regarded as a voting scheme, the proposed cascade algorithm can take advantage of the properties of voting method such as the Hough transform, i.e., the robustness against heavy noises and clutters, and ability of detecting multiple trajectories simultaneously. The experimental tests using real image sequences obtained in a past observation campaign demonstrate the effectiveness of the proposed method.

关键词： Space debris Geosynchronous Earth Orbit (GEO) Image detection voting algorithm

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An Optimal Parallel Average voting For Fault-Tolerant Control Systems

An Optimal Parallel Average Voting For Fault-Tolerant Contro...

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International Conference on Networking and Information Technology (ICNIT)

作者： Karimi, Abbas Faraneh, Zarafshan Jantan, Adznan B. Ramli, Abdul Rahman B. Saripan, M. Iqbal B. Islamic Azad Univ Fac Engn Dept Comp Engn Arak Branch Tehran Iran Islamic Azad Univ Young Res Club Arak Branch Tehran Iran Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang Malaysia

ISBN: (纸本)9781424475797;9781424475780

Fault-tolerant systems are such systems that can continue their operation, even in presence of faults. Redundancy as one of the main techniques in implementation of fault-tolerant control systems uses voting algorithms to choose the most appropriate value among multiple redundant and probably faulty results. Average (mean) voter is one of the commonest voting methods which is suitable for decision making in highly-available and long-missions applications in which the availability and speed of the system is critical. In this paper, we introduce a new generation of average voter based on parallel algorithms. Since parallel algorithms normally have high processing speed and are especially appropriate for large scale systems, we have used them to achieve an optimal parallel average voting algorithm with a time complexity of O (log n) with n/logn processors on EREW shared-memory for applications logn where the size of input space is large.

关键词： Fault-tolerant voting algorithm Parallel algorithm Divide and Conquer

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Multi-scale affined-HOF and dimension selection for view-unconstrained action recognition

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APPLIED INTELLIGENCE 2020年第5期50卷 1468-1486页

作者： Dinh Tuan Tran Yamazoe, Hirotake Lee, Joo-Ho Ritsumeikan Univ Grad Sch Informat Sci & Engn Kyoto Japan Ritsumeikan Univ Coll Informat Sci & Engn Kyoto Japan

In this paper an action recognition method that can adaptively handle the problems of variations in camera viewpoint is introduced. Our contribution is three-fold. First, a space-sampling algorithm based on affine transform in multiple scales is proposed to yield a series of different viewpoints from a single one. A histogram of dense optical flow is then extracted over each fixed-size patch for a given generated viewpoint as a local feature descriptor. Second, a dimension selection procedure is also proposed to retain only the dimensions that have distinctive information and discard the unnecessary ones in the feature vector space. Third, to adapt to a situation in which video data in multiple viewpoints are used for training;an extended method with a voting algorithm is also introduced to increase the recognition accuracy. By conducting experiments using both simulated and realistic datasets (), the proposed method is validated. The method is found to be accurate and capable of maintaining its accuracy under a wide range of viewpoint changes. In addition, the method is less sensitive to variations in subject scale, subject position, action speed, partial occlusion, and background. The method is also validated by comparing with state-of-the-art view-invariant action recognition methods using well-known i3DPost and MuHAVi public datasets.

关键词： Action recognition View-invariant Affine transform Histogram of optical flow Dimension selection voting algorithm

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