{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:35:22Z","timestamp":1760240122723,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,21]],"date-time":"2019-02-21T00:00:00Z","timestamp":1550707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["71801196"],"award-info":[{"award-number":["71801196"]}]},{"name":"National Major Project","award":["2017ZX06002006"],"award-info":[{"award-number":["2017ZX06002006"]}]},{"name":"Open Research Program from CAS Key Laboratory of Solar Activity in National Astronomical Observatories","award":["KLSA201803"],"award-info":[{"award-number":["KLSA201803"]}]},{"name":"China Postdoctoral Science Foundatuion","award":["2018M631606"],"award-info":[{"award-number":["2018M631606"]}]},{"name":"National Science Key Lab Fund project","award":["6142212180308"],"award-info":[{"award-number":["6142212180308"]}]},{"name":"Pre-Research Project of Equipment Development Department of People\u2018s Republic of China Central Military Commission","award":["41404060103"],"award-info":[{"award-number":["41404060103"]}]},{"name":"Basic Research Program of People\u2018s Republic of China Ministry of Industry and Information Technology","award":["A0920132002"],"award-info":[{"award-number":["A0920132002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault diagnostic software is required to respond to faults as early as possible in time-critical applications. However, the existing methods based on early diagnosis are not adequate. First, there is no common standard to quantify the response time of a fault diagnostic software to the fault. Second, none of these methods take into account how the objective to improve the response time may affect the accuracy of the designed fault diagnostic software. In this work, a measure of the response time is provided, which was formulated using the time complexity of the algorithm and the signal acquisition time. Model optimization was built into the designed method. Its objective was to minimize the response time. The constraint of the method is to guarantee diagnostic accuracy to no less than the required accuracy. An improved feature selection method was used to solve the optimization modeling. After that, the design parameter of the optimal quick diagnostic software was obtained. Finally, the parametric design method was evaluated with two sets of experiments based on real-world bearing vibration data. The results demonstrated that optimal quick diagnostic software with a pre-defined accuracy could be obtained through the parametric design method.<\/jats:p>","DOI":"10.3390\/s19040910","type":"journal-article","created":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T03:49:44Z","timestamp":1550807384000},"page":"910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Parametric Design Method for Optimal Quick Diagnostic Software"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9214-257X","authenticated-orcid":false,"given":"Xiao-jian","family":"Yi","sequence":"first","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Department of Overall Technology, China North Vehicle Research Institute, Beijing 100072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,21]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Hatami, N., and Chira, C. (2013, January 16\u201319). Classifiers with a reject option for early time-series classification. Proceedings of the IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), Singapore.","key":"ref_1","DOI":"10.1109\/CIEL.2013.6613134"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3167","DOI":"10.1109\/TIE.2011.2167110","article-title":"A PLS-Based Statistical Approach for Fault Detection and Isolation of Robotic Manipulators","volume":"59","author":"Muradore","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.arcontrol.2011.03.007","article-title":"Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems","volume":"35","author":"Lee","year":"2011","journal-title":"Annu. Rev. Control"},{"key":"ref_4","first-page":"1","article-title":"Application of a first-order linear system\u2019s stochastic resonance in fault diagnosis of rotor shaft","volume":"33","author":"Leng","year":"2014","journal-title":"J. Vib. Shock"},{"unstructured":"Ou, L., Li, D., and Zeng, X. (2013, January 16\u201317). Ship Propulsion Fault Diagnosis System Design Based on Remote Network. Proceedings of the Fifth International Conference on Measuring Technology and Mechatronics Automation, Hong Kong, China.","key":"ref_5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2015.08.023","article-title":"Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review","volume":"70\u201371","author":"Chen","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s00170-008-1563-9","article-title":"Machine fault diagnosis using a cluster-based wavelet feature extraction and probabilistic neural networks","volume":"42","author":"Yu","year":"2009","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29363","DOI":"10.3390\/s151129363","article-title":"Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution","volume":"15","author":"Jia","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1177\/0954406212457892","article-title":"The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum","volume":"227","author":"Jiang","year":"2013","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1016\/j.ymssp.2010.12.002","article-title":"Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum","volume":"25","author":"Ming","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","first-page":"1","article-title":"Weak Fault Feature Extraction of Rolling Bearings Using Local Mean Decomposition-Based Multilayer Hybrid Denoising","volume":"99","author":"Yu","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","first-page":"24","article-title":"Research on a extraction method for weak fault signal and its application","volume":"20","author":"Yong","year":"2007","journal-title":"J. Vib. Eng."},{"key":"ref_13","first-page":"529","article-title":"Theory and applications of weak signal non-linear detection method for incipient fault diagnosis of mechanical equipments","volume":"24","author":"Xu","year":"2011","journal-title":"J. Vib. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.ymssp.2012.06.008","article-title":"Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings","volume":"33","author":"Dong","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.ymssp.2011.11.021","article-title":"Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines","volume":"28","author":"He","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"doi-asserted-by":"crossref","unstructured":"Guo, W., Zhou, Z., and Chen, C. (2016, January 19\u201321). Cascaded and parallel stochastic resonance for weak signal detection and its simulation study. Proceedings of the Prognostics and System Health Management Conference (PHM-Chengdu), Chengdu, China.","key":"ref_16","DOI":"10.1109\/PHM.2016.7819839"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ymssp.2017.04.006","article-title":"Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system","volume":"96","author":"Liu","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1006\/mssp.2002.1470","article-title":"The Application of Stochastic Resonance Theory for Early Detecting Rub-impact of Rotor System","volume":"17","author":"Hu","year":"2003","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.measurement.2013.09.008","article-title":"Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise","volume":"47","author":"Shi","year":"2014","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TIE.2014.2327555","article-title":"Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach","volume":"62","author":"Amar","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1109\/TIE.2017.2762623","article-title":"Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines","volume":"65","author":"Ciabattoni","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_22","first-page":"211","article-title":"Early Fault Classification in Dynamic Systems Using Case-Based Reasoning","volume":"Volume 4177","author":"Alonso","year":"2005","journal-title":"Current Topics in Artificial Intelligence, Proceedings of the 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, Santiago de Compostela, Spain, 16\u201318 November 2005"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10115-015-0826-7","article-title":"Minimizing response time in time series classification","volume":"46","author":"Ando","year":"2016","journal-title":"Knowl. Inf. Syst."},{"unstructured":"Shi, W.W., Yan, H.S., and Ma, K.P. (2005, January 18\u201321). A new method of early fault diagnosis based on machine learning. Proceedings of the 2005 International Conference on Machine Learning and Cybernetics, Guangzhou, China.","key":"ref_24"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3390\/s120100189","article-title":"Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks","volume":"12","author":"Wan","year":"2012","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/0005-1098(76)90041-8","article-title":"A survey of design methods for failure detection in dynamic systems","volume":"12","author":"Willsky","year":"1976","journal-title":"Automatica"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3901\/JME.2013.01.063","article-title":"Basic Research on Machinery Fault Diagnosis\u2014What is the Prescription","volume":"49","author":"Wang","year":"2013","journal-title":"J. Mech. Eng."},{"doi-asserted-by":"crossref","unstructured":"Sipser, M. (1996). Introduction to the Theory of Computation: Preliminary Edition, PWS Pub. Co.","key":"ref_28","DOI":"10.1145\/230514.571645"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csda.2009.04.009","article-title":"Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap","volume":"53","author":"Kim","year":"2009","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"943","DOI":"10.4028\/www.scientific.net\/AMM.813-814.943","article-title":"Automobile Gearbox Fault Diagnosis Using Naive Bayes and Decision Tree Algorithm","volume":"813\/814","author":"Sreenath","year":"2015","journal-title":"Appl. Mech. Mater."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1006\/mssp.2001.1454","article-title":"Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms","volume":"16","author":"Jack","year":"2002","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.engappai.2003.09.006","article-title":"Bearing fault detection using artificial neural networks and genetic algorithm","volume":"16","author":"Samanta","year":"2003","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","article-title":"Feature selection for classification","volume":"1","author":"Dash","year":"1997","journal-title":"Intell. Data Anal."},{"unstructured":"Kira, K., and Rendell, L.A. (1992, January 12\u201316). The feature selection problem: Traditional methods and a new algorithm. Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, CA, USA.","key":"ref_34"},{"unstructured":"Liu, H., and Setiono, R. (1996, January 4\u20137). Feature selection and classification\u2014A probabilistic wrapper approach. Proceedings of the 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Fukuoka, Japan.","key":"ref_35"},{"key":"ref_36","first-page":"1439","article-title":"Use of the zero norm with linear models and kernel methods","volume":"3","author":"Weston","year":"2003","journal-title":"J. Mach. Learn. Res."},{"unstructured":"Doak, J. (2019, February 13). An Evaluation of Feature Selection Methods and Their Application to Computer Security. Available online: https:\/\/escholarship.org\/uc\/item\/2jf918dh.","key":"ref_37"},{"doi-asserted-by":"crossref","unstructured":"Siedlecki, W., and Sklansky, J. (1988). On Automatic Feature Selection. Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., Inc.","key":"ref_38","DOI":"10.1142\/S0218001488000145"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study","volume":"64\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"64","DOI":"10.21595\/vp.2017.19153","article-title":"Fault Diagnosis of Rolling Element Bearing Using Na\u00efve Bayes Classifier","volume":"19","author":"Yi","year":"2017","journal-title":"J. Vibroeng."},{"doi-asserted-by":"crossref","unstructured":"Murty, M.N., and Devi, V.S. (2011). Pattern Recognition: An Algorithmic Approach, Springer.","key":"ref_41","DOI":"10.1007\/978-0-85729-495-1"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ymssp.2013.09.003","article-title":"Wavelet leaders multifractal features based fault diagnosis of rotating mechanism","volume":"43","author":"Du","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.1109\/TIE.2013.2273471","article-title":"Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis","volume":"61","author":"Jin","year":"2014","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.measurement.2015.03.017","article-title":"A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM","volume":"69","author":"Zhang","year":"2015","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:33:52Z","timestamp":1760186032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,21]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19040910"],"URL":"https:\/\/doi.org\/10.3390\/s19040910","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,2,21]]}}}