Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm

Ravikumar, K.; Madhusudana, C K; Kumar, Hemantha; Gangadharan, K. V.

doi:10.1016/j.jestch.2021.08.005

Cited by 24 publications

(13 citation statements)

References 28 publications

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“…The K-Nearest Neighbors (KNN) algorithm has been first proposed in 1967 as a decision rule for classifying the nearest instance from a set of previously classified instances to an unclassified sample instance [56]. KNN is an extremely simple supervised classification approach which requires few assumptions, especially in pattern recognition [57]. KNN…”

Section: Ii6 K-nearest Neighbors (Knn)mentioning

confidence: 99%

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

AFIA

Gougam

Rahmoune

et al. 2023

Preprint

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Air compressors have become critical equipment in different industrial applications such as metallurgy, mining, machinery manufacturing, petrochemical industry, transportation, etc. However, because of their complex structure and often harsh working environment, air compressors inevitably face a variety of faults and failures during their operation. Therefore, intelligent diagnostic techniques are crucially important for early fault recognition and detection to avoid industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is proposed based on several approaches, mainly: Maximal overlap discrete wavelet packet transform (MODWPT) and time domain features for feature extraction, weighted superposition attraction (WSA) for feature selection and random forest (RF), ensemble tree (ET) K-nearest neighbors (KNN) as classifiers. The proposed approach is applied to real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states. According to our approach, the data signals are decomposed by MODWPT into several nodes. Then, the time domain features are calculated for each node to construct the feature matrix for each air compressor health state. After that, WSA is applied to every matrix in the feature selection step. Finally, KNN, ET and RF are used to calculate the classification accuracy and give the confusion matrix. Compared with the robust empirical mode decomposition (REMD), the experimental results prove the effectiveness of the proposed approach to detect, identify and classify all air compressor faults.

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Section: Ii6 K-nearest Neighbors (Knn)mentioning

confidence: 99%

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

AFIA

Gougam

Rahmoune

et al. 2023

Preprint

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“…K.N. Ravikumar et al [2] in their study they Tested four stroke IC engine gearbox which is fitted with an eddy current dynamometer. To avoid undesirable excitation setup is supported with base frame.…”

Section: Related Workmentioning

confidence: 99%

Experimental Setup for Gear Fault Diagnosis using Machine Learning

Salunkhe¹

2022

IJSREM

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–There are various types of defects occur in the gear such as scoring, face wear, tooth breakage, pitting, root crack etc. Early detection of such defects is very crucial to prevent the system from sudden breakdown. Fault diagnosis is a process used for preventing breakdown. For such a fault diagnosis. we are analysing vibrations of gears. Scope of this work is to take the vibration signals for various faulty gears along with healthy gears. To classify the defects in spur gear first we need dataset for study. This paper provides the information of experimental setup to generate the dataset of vibration signals of faulty gears and healthy gear. In addition, this paper provides the details of fault generation in spur gear. Key Words: Spur Gear, Fault Diagnosis, Dataset, Vibration Signal, Tooth Breakage, Face Wear, Machine Learning.

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“…With this motivation, the concept of developing a Tool Condition Monitoring (TCM) system has evolved. A significant goal of TCM is to recognize the deterioration of the cutting edge, which subsequently improves the value of the job [ 14 , 15 ]. TCM generally aims at monitoring the behavior of the cutting tool in terms of machining datasets collected through various sensors such as microphones [ 16 ], dynamometers [ 17 ], accelerometers [ 18 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Belief Network for Tool Faults Recognition

Kale

Wahul

Patange³

et al. 2023

Sensors

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The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.

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Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm

Cited by 24 publications

References 28 publications

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

Experimental Setup for Gear Fault Diagnosis using Machine Learning

Development of Deep Belief Network for Tool Faults Recognition

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