Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump

Sakthivel, N. R.; Indira, V.; Nair, Binoy B.; Sugumaran, V.

doi:10.1504/ijgcrsis.2011.041458

Cited by 19 publications

(8 citation statements)

References 11 publications

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“…Machine learning involves three main steps, viz, feature extraction, feature selection, and feature classification. Features can be statistical features [3], auto regressive moving average (ARMA) features [4], histogram features [4] and wavelet features [5]. In the present study statistical features were used.…”

Section: Introductionmentioning

confidence: 99%

“…Naïve Bayes (NB) and Bayes Net (BN) were successfully applied for the fault classification monoblok centrifugal pump [4]. NB and BN often fail to produce a good estimate of the correct class probabilities; this may not be a requirement for many applications.…”

Section: Introductionmentioning

confidence: 99%

“…Even visual inspection can identify the faults; industry needs an automated procedure for the identification of the faults. One commonly used technique is artificial neural network (ANN) [4]. The condition monitoring problem is treated as generalization/classification problem based on the training pattern from the samples.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Jegadeeshwaran

Sugumaran

2015

Mechanical Systems and Signal Processing

Self Cite

176

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Jegadeeshwaran

Sugumaran

2015

Mechanical Systems and Signal Processing

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176

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“…There is one base station (central control unit), which will collect data from all 18 nodes and present it to the maintenance Engineers. Upon going through the literature, one can find that in machine learning based fault diagnosis, many features like statistical features [14], Histogram features [15], ARMA features [16], wavelet-DWT features [17], and classifiers (18) were used. In most of the study on fault diagnosis using machine learning approach, the focus was on achieving high classification accuracy so that when a fault occurs, it can be identified successfully.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

2018

Teh. vjesn.

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The breakdown in a machine created by a fault will greatly affect the plant operation. The frame work of fault diagnosis of machines using machine learning techniques is an established area. Here, the fault diagnosis system is implemented with the help of wireless sensor networks (WSN). Each machine in the plant is fitted with sensors (node) from which the fault diagnosis is carried out. The signals/messages from each node are transmitted to a base station, which acts as a central control unit for the entire plant. Fault diagnosis using WSN in a factory setup has few challenges. The major issue in WSN is the life time of the nodes, as they are situated far away from base station in many plants like thermal power plant, refinery, and petroleum industries. To address the life time of the nodes many researchers have developed many protocols like LEACH, SEP, ERP, HCR, HEED, and PEGASIS. The plants need customisation in terms of choosing suitable algorithms and choosing location of the base station within the plant for better life time of the nodes. This paper presents results of both experimental and simulation studies of a typical plant, where the vibration signals from each machine are acquired and through machine learning techniques the fault diagnosis is performed with the help of wireless sensor networks. For illustrative purpose, a well reported bearing fault diagnosis data set is taken up and fault diagnosis case study was performed from wireless sensor networks point of view (experimental study). Here, at every stage, the computational time is taken as a primary concern which affects the life time of the sensor nodes. Then, the WSN of 18 sensor nodes representing 18 machines with LEACH protocol is simulated in Matlab© to study the life time characteristics of each node while keeping base station at different locations. The life time of different nodes is heavily dependent on the location of the base station. Finding the right location of the base station for a given plant is another contribution of this work.

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“…There are many features available in literature, namely, histogram features [6,7], statistical features [7], and wavelet features [8,9]. In the present study statistical features were used for the fault diagnosis study.…”

Section: Introductionmentioning

confidence: 99%

Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A statistical learning approach

Jegadeeshwaran

Sugumaran

2015

Engineering Science and Technology, an International Journal

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a b s t r a c tIn automobile, brake system is an essential part responsible for control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the vehicle cum passenger's safety. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is essential. Vibration based condition monitoring using machine learning techniques are gaining momentum. This study is one such attempt to perform the condition monitoring of a hydraulic brake system through vibration analysis. In this research, the performance of a Clonal Selection Classification Algorithm (CSCA) for brake fault diagnosis has been reported. A hydraulic brake system test rig was fabricated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signal. The best feature set was identified for classification using attribute evaluator. The selected features were then classified using CSCA. The classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed. The Clonal Selection Classification Algorithm performs better and gives the maximum classification accuracy (96%) for the fault diagnosis of a hydraulic brake system.

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Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump

Cited by 19 publications

References 11 publications

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A statistical learning approach

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