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Static Analysis and Code Complexity Metrics as Early Indicators of Software Defects
2018
Journal of Software Engineering and Applications
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This article presents an exploratory study investigating whether the faults detected by static analysis tools combined with code complexity metrics can be used as software quality indicators and to build ...
The combination of code complexity metrics with static analysis fault density was used to predict the pre-release fault density with an accuracy of 78.3%. ...
component analysis (PCA) technique. ...
doi:10.4236/jsea.2018.114010
fatcat:klw2xazgsfb5lfmgr5wxbny5hq
Novel Approach to Rotating Machinery Diagnostics Based on Principal Component and Residual Matrix Analysis
2012
ISRN Mechanical Engineering
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This paper presents a new and sensitive approach, to detect faults in rotating machines; based on principal component techniques and residual matrix analysis (PCRMA) of the vibration measured signals. ...
A variety of techniques have been employed over the past several decades for fault detection and identification in such machinery. ...
Early fault detection would eliminate consequential damages of motors and reduce outage time and costs of repairs. ...
doi:10.5402/2012/715893
fatcat:h6gaciyskfasda7fdllqkv5orm
Combining Neural Methods and Knowledge-Based Methods in Accident Management
2012
Advances in Artificial Neural Systems
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Promising results in early fault detection are achieved. The operators are provided by added information value to be able to detect anomalies in an early stage already. ...
Early fault detection and information visualization are important key issues in accident management also today. ...
The principal component analysis (PCA) is a useful tool for finding relevant variables for the system and the model. ...
doi:10.1155/2012/534683
fatcat:r6nsepxsbbesjg7m2u24uqxabq
Multisensor Fault Detection and Isolation Using Kullback Leibler Divergence: Application to Data Vibration Signals
2017
2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
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Data-driven methods are effective for feature extraction and feature analysis using statistical techniques. ...
In the proposal, the Principal Component Analysis (PCA) method is used to extract the features and to reduce the data dimension. ...
forces, they provide the most salient information for the early detection of bearing faults. ...
doi:10.1109/sdpc.2017.65
fatcat:bauwpkh2mjdjvib7a57moxelpm
Wavelet support vector machine for induction machine fault diagnosis based on transient current signal
2008
Expert systems with applications
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In this work, principal component analysis (PCA) and kernel PCA are performed to reduce the dimension of features and to extract the useful features for classification process. ...
This paper presents establishing intelligent system for faults detection and classification of induction motor using wavelet support vector machine (W-SVM). ...
Component analysis
Principal component analysis (PCA) Given a set of centered input vectors x t (t = 1, . . . ...
doi:10.1016/j.eswa.2007.06.018
fatcat:6leb7hk7t5envpxpujo7jkax5y
A Review of Vibration Analysis Techniques for Rotating Machines
2015
International Journal of Engineering Research and
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Vibration analysis is a technique used for condition monitoring of the machine. Effective vibration signal extracting techniques have a critical role in efficiently diagnosing a rotating machine. ...
Many vibration signal extracting techniques have been proposed during past some years. ...
The second order cyclostationary is an effective tool used for detecting early faults in gear system. It has been used for early diagnosis in gear system [31] . ...
doi:10.17577/ijertv4is030823
fatcat:54ypwcwvq5fsbo4ytp5z6c3x3u
Diagnosis of defects by principal component analysis of a gas turbine
2020
SN Applied Sciences
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This study examines the application of the Principal Component Analysis (PCA) technique to detect the failures in complex industrial processes such as gas turbines used for electric power generation. ...
The results obtained will aid to confirm the performance of the linear PCA method in the field of early failure detection. ...
Principal component analysis Principal Component Analysis (PCA) is a part of the group of multidimensional descriptive methods called factorial methods. ...
doi:10.1007/s42452-020-2796-y
fatcat:iz424ao3kfdshkgs36g7xlba6a
PRINCIPAL COMPONENT ANALYSIS BASED APPROACH FOR FAULT DIAGNOSIS IN PNEUMATIC VALVE USING DAMADICS BENCHMARK SIMULATOR
2014
International Journal of Research in Engineering and Technology
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The performance of the proposed ANN model is improved by proposing suitable dimensionality reduction technique like Principal Component Analysis (PCA). ...
The proposed approach uses back propagation algorithm (BPN) to detect and diagnose the faults in pneumatic valve under normal and faulty operating conditions. ...
Fig-2: Schematic Representation of ANN Model for Fault Diagnosis
PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) is one of the feature extraction techniques. ...
doi:10.15623/ijret.2014.0319125
fatcat:rzsflz6gofdj3d4d7cy7uqwxca
Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods
2019
Urban Rail Transit
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Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. ...
The research result consists of the early prediction of rail contact fatigue. ...
Acknowledgements The authors would like to acknowledge the Germany Railway Company (DB Systemtechnik GmbH) and WITT Elektronik GmbH for their experimental and financial supports. ...
doi:10.1007/s40864-019-0105-0
fatcat:mosk7uudofh6xmnu7qi5nxgdny
Rotary Machines Fault Diagnosis based on Principal Component Analysis
2021
Engineering Research Journal
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The condition monitoring technique based on vibration analysis has the potential to detect and diagnose a great number of early stage faults. ...
In this paper, a proposed method based on the Principal Component Analysis (PCA) is presented to produce uncorrelated Principal Components (PCs) to identify the healthy and different faulty cases. ...
It was observed that the PCA based technique is a good fit for early fault detection compared to the conventional methods. ...
doi:10.21608/erj.2021.193822
fatcat:moxas6hd5bholpya2b3nw3kisq
Wind turbine generator slip ring damage detection through temperature data analysis
2019
Diagnostyka
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It is therefore valuable developing analysis techniques for this kind of data, with the aim of detecting incoming faults as early as possible. ...
A principal component regression is adopted, targeting the temperature collected at the slip ring. ...
Acknowledgement The authors thank the Lucky Wind company for providing the data sets employed in this work. ...
doi:10.29354/diag/109968
fatcat:geb3mbzlqbgsznja7i3mg7nuey
Vibration based Fault Diagnosis Techniques for Rotating Mechanical Components: Review Paper
2018
IOP Conference Series: Materials Science and Engineering
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Different signal processing techniques are used for processing these signals. The various techniques used for fault diagnosis based on vibration analysis method are discussed in this paper. ...
Signature of the fault in the machine is carried by the vibration signal. It is possible to have early fault detection by analysing these vibration signals. ...
Makis, 2006, have used dynamic principal component analysis (PCA) and condition monitoring technique for the gear failure diagnosis based on vector autoregressive modeling of highfrequency vibration data ...
doi:10.1088/1757-899x/376/1/012109
fatcat:emw2magtdbe3tc7l6d2tetys5e
Recent Techniques to Identify the Stator Fault Diagnosis in Three Phase Induction Motor
2012
International Journal of Energy Optimization and Engineering
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The results of the analysis also verified through Power Decomposition Technique (PDT) in Matlab /SIMULINK. The results are compatible with the published results for known faults. ...
In this paper, the authors diagnose the turn-to-turn faults condition of the stator through symmetrical component analysis. ...
The principal merit of symmetrical component analysis is that a relatively complicated problem can be solved by developing no more than three balanced network for three phase unbalance system. ...
doi:10.4018/ijeoe.2012100107
fatcat:rzliksr5qnd2tk7kf6wh6wjq3m
Process performance monitoring using multivariate statistical process control
1996
IEE Proceedings - Control Theory and Applications
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The bases of MSPC are the projection techniques of principal components analysis and projection to latent structures. ...
Classical univariate statistical techniques have focused on the monitoring of one quality variable at a time and are not appropriate for analysing process data where variables exhibit collinear behaviour ...
Principal components analysis The primary objectives of principal components analysis (PCA) are data summarisation, classification of variables, outlier detection, early warning of potential malfunctions ...
doi:10.1049/ip-cta:19960321
fatcat:qmw6gv2fvjgdnmu7klg2vuo554
Induction Motor Stator Fault Classification Using PCA-ANFIS Technique
2020
Elektrika: Journal of Electrical Engineering
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The classification of stator fault in a three-phase induction motor based on Adaptive neuro-fuzzy inference system (ANFIS) in combination with Principal Component Analysis (PCA) is proposed in this study ...
Three principal components, which severs as input for the ANFIS, were used to represent the entire data. ...
One of the methods used in feature extraction is Principal Component Analysis (PCA). ...
doi:10.11113/elektrika.v19n1.209
fatcat:vepnja6j5fcsnnhlola6rzsona
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