Fault Detection of Mechanical Equipment Failure Detection Using Intelligent Data Analysis

  • Maksim Andreevich Kovito Department of Informatics, Institute of Space and Information Technologies, Siberian Federal University, Krasnoyarsk, Russia
Keywords: Data Mining, Method, Mechanical Equipment, Sensor, System, Malfunction


Poor maintenance of machinery in manufacturing plants has always been an important link in the production process. In addition to computer technology, artificial intelligence technologies and various intelligent sensors are widely used in manufacturing industries. The amount of data generated by production machines and equipment at all stages of the production process is also growing rapidly, and it is particularly important to analyze the data generated by these devices in order to detect and even predict malfunctions. Intelligent data mining provides advanced data analysis techniques for this purpose. This article introduces the basic concepts of data mining, its processes, the main data mining technologies, and provides recommendations for applying data mining to detect failures in devices.


Download data is not yet available.


Tao F., Qi Q. New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Transactions on Systems, Man and Cybernetics; 2017. http://dx.doi.org/10.1109/TSMC.2017.2723764

Yin S., Kaynak O. Big data for modern industry: challenges and trends [point of view]. Proc IEEE. 2015; 103(2): 143-146.

Harding J. et al. Data Mining in Manufacturing: A Review. VDM Verlag; 2006: 969-976.

Wang K. Tong et al. Review on Application of Data Mining in Product Design and Manufacturing. International Conference on Fuzzy Systems and Knowledge Discovery; 2007: 613-618.

Hand D. J. Data Mining. Encyclopedia of Environmetrics. 2006; 2.

Yu C. H. Exploratory data analysis. Methods. 1977; 2: 131-160.

Seber G. A. F., Lee A. J. Linear regression analysis: John Wiley & Sons; 2012.

Aiken L.S., West S.G., Reno R.R. Multiple regression: Testing and interpreting interactions: Sage; 1991.

Hansen L.K., Salamon P. Neural network ensembles. IEEE Transactions on Pattern Analysis & Machine Intelligence. 1990; (10): 993-1001.

Keller J.M., Gray M.R., Givens J.A. A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man and Cybernetics. 1985; 15(4): 580-585.

Hosmer Jr.D.W., Lemeshow S., Sturdivant R.X. Applied logistic regression: John Wiley & Sons; 2013. Информатика. Экономика. Управление/Informatics, Economics, Management 2022; 1(2) http://oajiem.com/0132

Munro D.D. Forest growth models-a prognosis. Growth models for tree and stand simulation. Research Note 30. Department of Forest Yield Research. Royal College of Forestry: Stockholm. 1974; 30: 7-21.

Kass R. E., Raftery A. E. Bayes factors. Journal of the American statistical association. 1995; 90(430): 773-795.

Zhang T., Ramakrishnan R., Livny M. BIRCH: A new data clustering algorithm and its applications. Data Mining and Knowledge Discovery. 1997; 1(2): 141-182.

Inokuchi A., Washio T., Motoda H. An apriory-based algorithm for mining frequent substructures from graph data. European conference on principles of data mining and knowledge discovery: Springer, Berlin, Heidelberg; 2000: 13-23.

Freitas A.A. A genetic programming framework for two data mining tasks: classification and generalized rule induction. Genetic Programming 1997: Proc 2nd Annual Conf. Morgan Kaufmann; 1997: 96-101.

Zheng X., Levine D., Shen J. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012; 28(24): 3326-3328.

Hongjin Z. Fault diagnosis of hydraulic System of road header based on Fuzzy Neural network. Nanjing University of Science and Technology. 2013.

Xin Xuming. Data Mining System based on process Monitoring and its application. Shanghai: Donghua University. 2004.

JiaoPeng Sha. Research on intelligent device Fault diagnosis based on matrix weighted association rules. Hebei: Yanshan University. 2012.

Hong S. Lim W.Y., Cheong T. et al. Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing e-Diagnostics. IEEE Transactions on Semiconductor Manufacturing. 2012; 25(1):83-93.

Wang. Application of Decision Tree algorithm in mechanical equipment fault diagnosis system. Wuhan: Huazhong University of Science and Technology. 2013.

XueYong Shu. Application of vibration fault diagnosis of rotating equipment based on data Mining. Jilin: Jilin University. 2013.

Wang S., Sun X., Li C. Wind turbine gearbox fault diagnosis method based on Riemannian manifold. Mathematical Problems in Engineering. 2014; 16(4): 835-892.

Jiao B., Xu Zh. Parameters optimization of LSSVM and application in fault diagnosis of wind power gearbox. Control Engineering of China. 2012; 19(4): 681-686. Информатика. Экономика. Управление//Informatics, Economics, Management 2022; 1(2) http://oajiem.com/0133

Shen B., Liang X., Ouyang Y. et al. Stepdeep: a novel spatial-temporal mobility event prediction framework based on deep neural network. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM; 2018: 724-733.

Arora N., Cook J., Kumar R. et al. Hard to Park: Estimating Parking Difficulty at Scale. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM; 2019: 2296-2304

How to Cite
Kovito, M. A. (2022). Fault Detection of Mechanical Equipment Failure Detection Using Intelligent Data Analysis. Journal of Systems Engineering and Information Technology (JOSEIT), 1(2), 62-66. https://doi.org/10.29207/joseit.v1i2.4943