{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:12:15Z","timestamp":1771650735868,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,17]],"date-time":"2018-12-17T00:00:00Z","timestamp":1545004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory Opening Funding HIT.KLOF","award":["2018.076"],"award-info":[{"award-number":["2018.076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions.<\/jats:p>","DOI":"10.3390\/s18124460","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T02:15:59Z","timestamp":1545099359000},"page":"4460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3862-6987","authenticated-orcid":false,"given":"Yunzhao","family":"Jia","sequence":"first","affiliation":[{"name":"School of Astronautics, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minqiang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Astronautics, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rixin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Astronautics, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ymssp.2017.06.012","article-title":"A review on data-driven fault severity assessment in rolling bearings","volume":"99","author":"Cerrada","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s11760-008-0061-8","article-title":"Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns","volume":"3","author":"Rao","year":"2008","journal-title":"Signal Image Video Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/TMECH.2006.878544","article-title":"Symbolic time-series analysis for anomaly detection in mechanical systems","volume":"11","author":"Khatkhate","year":"2006","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1109\/TIM.2016.2564078","article-title":"Health Condition Monitoring and Early Fault Diagnosis of Bearings Using SDF and Intrinsic Characteristic-Scale Decomposition","volume":"65","author":"Li","year":"2006","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.ymssp.2016.12.040","article-title":"A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection","volume":"91","author":"Li","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jprocont.2016.05.007","article-title":"An on-line SAX and HMM-based anomaly detection and visualization tool for early disturbance discovery in a dynamic industrial process","volume":"44","author":"Yiakopoulos","year":"2016","journal-title":"J. Process. Control."},{"key":"ref_8","unstructured":"Das, G., Lin, K.I., Mannila, H., Renganathan, G., and Symth, P. (1998, January 27\u201331). Rule discovery from time series. Proceedings of the 4th International Conference on Knowledge Discovery Data Mining, New York, NY, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1177\/1077546313479992","article-title":"Zero crossing and coupled hidden Markov model for a rolling bearing performance degradation assessment","volume":"20","author":"Liu","year":"2013","journal-title":"J. Vib. Control"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fu, T.-C., Hung, Y.-K., and Chung, F.-L. (2017, January 21\u201323). Improvement Algorithms of Perceptually Important Point Identification for Time Series Data Mining. Proceedings of the 2017 IEEE International Conference on Soft Computing and Machine Intelligence, Changsha, China.","DOI":"10.1109\/ISCMI.2017.8279589"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.ymssp.2015.08.030","article-title":"Fault diagnosis in spur gears based on Genetic Algorithm and random forest","volume":"70\u201371","author":"Cerrada","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1016\/j.engappai.2008.01.005","article-title":"Stock time series visualization based on data point importance","volume":"21","author":"Fu","year":"2008","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/TEVC.2004.832863","article-title":"An Evolutionary Approach to Pattern-Based Time Series Segmentation","volume":"8","author":"Chung","year":"2004","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.jtrangeo.2016.06.010","article-title":"Perceptually important points of mobility patterns to characterise bike sharing systems: The Dublin case","volume":"54","author":"Nogal","year":"2016","journal-title":"J. Transp. Geogr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, T.-C., Chung, F.-L., and Ng, C.M. (2006, January 26\u201329). Financial Time Series Segmentation based on Specialized Binary Tree Representation. Proceedings of the Conference on Data Mining\u2019 06, Las Vegas, NV, USA.","DOI":"10.2991\/jcis.2006.30"},{"key":"ref_16","unstructured":"Nguyen, T.S., and Duong, T.A. (2010, January 7\u20139). An improvement of PIP for Time Series Dimensionality Reducion and its Index Structure. Proceedings of the 2010 Second International Conference on Knowledge and Systems Engineering, Hanoi, Vietnam."},{"key":"ref_17","first-page":"257","article-title":"A tutorial on hidden Markov models and selected applications in speech recognition","volume":"77","author":"Rabiner","year":"1989","journal-title":"Read. Speech Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jsv.2005.05.020","article-title":"Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model","volume":"291","author":"Li","year":"2006","journal-title":"J. Sound Vib."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ymssp.2015.10.003","article-title":"Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment","volume":"72\u201373","author":"Jiang","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1016\/j.ymssp.2011.01.013","article-title":"Detection and diagnosis of bearing and cutting tool faults using hidden Markov models","volume":"25","author":"Boutros","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TR.2012.2194177","article-title":"A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models","volume":"61","author":"Medjaher","year":"2012","journal-title":"IEEE Trans. Reliab."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3175","DOI":"10.1177\/1077546315627934","article-title":"A hybrid hidden Markov model towards fault detection of rotating components","volume":"23","author":"Sadhu","year":"2016","journal-title":"J. Vib. Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.ymssp.2015.01.033","article-title":"Rolling element bearings diagnostics using the Symbolic Aggregate approXimation","volume":"60\u201361","author":"Georgoulas","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ymssp.2016.10.034","article-title":"A SVM framework for fault detection of the braking system in a high speed train","volume":"87","author":"Liu","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.ymssp.2014.08.007","article-title":"Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines","volume":"52\u201353","author":"Jegadeeshwaran","year":"2015","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1109\/TIE.2017.2745473","article-title":"Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network","volume":"65","author":"Shao","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, Q.S., Shen, Y.H., Li, H., and Xu, F.Y. (2018). New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network. Sensors, 18.","DOI":"10.3390\/s18020337"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4460\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:34:24Z","timestamp":1760196864000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/12\/4460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,17]]},"references-count":27,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["s18124460"],"URL":"https:\/\/doi.org\/10.3390\/s18124460","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,17]]}}}