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Static Analysis and Code Complexity Metrics as Early Indicators of Software Defects

Safa Omri, Pascal Montag, Carsten Sinz
2018 Journal of Software Engineering and Applications  
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

A. Abouhnik, Ghalib R. Ibrahim, R. Shnibha, A. Albarbar
2012 ISRN Mechanical Engineering  
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

Miki Sirola, Jaakko Talonen
2012 Advances in Artificial Neural Systems  
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

Claude Delpha, Demba Diallo, Tianzhen Wang, Jie Liu, Zelig Li
2017 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)  
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

A WIDODO, B YANG
2008 Expert systems with applications  
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

Saurabh Singh, Dr. Manish Vishwakarma
2015 International Journal of Engineering Research and  
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

Fenghour Nadir, Hadjadj Elias, Bouakkaz Messaoud
2020 SN Applied Sciences  
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

A.Kowsalya .
2014 International Journal of Research in Engineering and Technology  
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

Mykola Sysyn, Ulf Gerber, Olga Nabochenko, Dmitri Gruen, Franziska Kluge
2019 Urban Rail Transit  
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

M. Elsamanty, W. S. Salman, A. A. Ibrahim
2021 Engineering Research Journal  
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

Davide Astolfi, Francesco Castellani, Francesco Natili
2019 Diagnostyka  
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

Sujesh Kumar, M Lokesha, Kiran Kumar, K R Srinivas
2018 IOP Conference Series: Materials Science and Engineering  
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

K. Vinoth Kumar, S. Suresh Kumar, A. Immanuel Selvakumar, R. Saravana Kumar
2012 International Journal of Energy Optimization and Engineering  
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

E.B. Martin, A.J. Morris, J. Zhang
1996 IEE Proceedings - Control Theory and Applications  
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

Ayodele Isqeel Abullateef, Mohammed Faiz Sanusi, Olabanji Sunday Fagbolagun
2020 Elektrika: Journal of Electrical Engineering  
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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