A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis

Alaei, Hesam Komari; Salahshoor, Karim; Alaei, Hamed Komari

doi:10.1007/s00500-012-0910-9

Cited by 25 publications

(8 citation statements)

References 22 publications

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“…In the case of images, these operations are useful to enhance some characteristics of the image for human visual inspection. For instance, recent works have proposed to use neural networks to segment suitable areas for bee foraging in satellite images [57] or fuzzy methods to segment time-series in chemical processes [58] or wood particles in panel production images [20].…”

Section: B Data Preprocessingmentioning

confidence: 99%

Computational intelligence for industrial and environmental applications

Labati

Genovese

Muñoz

et al. 2016

2016 IEEE 8th International Conference on Intelligent Systems (IS)

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Computational Intelligence (CI) techniques are being increasingly used for automatic monitoring and control systems, especially regarding industrial and environmental applications, due to their performance, their capabilities in fusing noisy or incomplete data obtained from heterogeneous sensors, and the ability in adapting to variations in the operational conditions. Moreover, the increase in the computational power and the decrease of the size of the computing architectures allowed a more pervasive use of CI techniques in a great variety of situations. In this paper, we propose a brief review of the most important CI techniques applied in each step of the design of a monitoring and control system for industrial and environmental applications, and describe how these techniques are integrated in the development of efficient industrial and environmental applications.

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Section: B Data Preprocessingmentioning

confidence: 99%

Computational intelligence for industrial and environmental applications

Labati

Genovese

Muñoz

et al. 2016

2016 IEEE 8th International Conference on Intelligent Systems (IS)

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“…If a certain type of sample is considered to be a fuzzy subset of the sample set of , the corresponding membership matrix will be a fuzzy membership matrix, which is denoted as = { }. Then has the following characteristics [10][11][12]:…”

Section: Features Extraction In Order Tracking Spectrummentioning

confidence: 99%

“…Hence, the vibration signal under the variable speed working conditions can be analyzed by order tracking, and the features can be extracted easily [6][7][8]. FCM is an unsupervised machine learning technique, which can objectively classify the categories with fuzzy characteristics through uncertainty description of the sample class and become an important tool in pattern recognition, image processing, fuzzy control, and other areas [9,10]. Because the fault classification is very vague in mechanical fault of diesel engine, we can use this method to solve the problem of pattern recognition in engine fault detection and isolation.…”

Section: Introductionmentioning

confidence: 99%

A Method Combining Order Tracking and Fuzzy C-Means for Diesel Engine Fault Detection and Isolation

Zeng

Zhang

Xiao

et al. 2015

Shock and Vibration

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Diesel engine works under variable speed conditions; fault symptoms are clearer in the angular/order domains than in the common time/frequency ones. In this paper, firstly, the acceleration signal of diesel engine is resampled by order tracking, in which the rotating speed is computed in every working cycle, and the order tracking spectrum is created in each interval’s speed; then different order band accumulated energy is computed as feature vector. After standardizing these features, the fuzzy c-means (FCM) is introduced to use them as input vector; the optimized classified matrix and clustering centers can be obtained using FCM iteration method; then the fault can be detected by calculating the approach degree between the unknown samples and the known ones. To validate the method, some experiments have been performed; the results show that the signal can be reconstructed, and the features of order band accumulated energy can reflect the information of different wear conditions in crank-shaft bearing; then the fault can be detected accurately. The method of nonentire work cycle is also introduced as a comparison with our method; the result shows our method has more accuracy classification.

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“…Mining the hidden state information in the data deeply provides a feasible and effective method for the unit state warning technology research. Zhang Z Y applied data mining to early warning of actual power station [2] , Fu W A studied the fault early warning model of extreme random tree algorithm [3] ; Han W L applied PCA method and MSET method to unit state early warning [4] . In addition, there are other studies on early warning methods such as correlation analysis, neural network, and so on [5][6] .…”

Section: Introductionmentioning

confidence: 99%

Fault warning method based on extreme learning regression and fuzzy reasoning

Qiu

Cao

et al. 2021

E3S Web Conf.

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Aiming at the problem of condition monitoring of thermal power units, a fault early warning method based on fuzzy learning machine is proposed. The extreme learning regression model between monitoring parameters is established by using real-time data. Then the estimated value is fuzzed and used for fuzzy reasoning, finally, the fault diagnosis results of the unit under small abnormal state are obtained. The simulation data of a 1000 MW unit is used for verification test and results show that the proposed method is reliable and accurate which is suitable for thermal unit state warning.

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A new integrated on-line fuzzy clustering and segmentation methodology with adaptive PCA approach for process monitoring and fault detection and diagnosis

Cited by 25 publications

References 22 publications

Computational intelligence for industrial and environmental applications

Computational intelligence for industrial and environmental applications

A Method Combining Order Tracking and Fuzzy C-Means for Diesel Engine Fault Detection and Isolation

Fault warning method based on extreme learning regression and fuzzy reasoning

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