{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T11:12:32Z","timestamp":1760181152909,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T00:00:00Z","timestamp":1606694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["2017R1D1A1B04036509"],"award-info":[{"award-number":["2017R1D1A1B04036509"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar frequency or if they were rarely observed in any system state. In order to estimate the system status with imbalanced state data characterized insufficient fault occurrences, this paper proposes a state prediction method that employs discrete state vectors (DSVs) for pattern extraction and then applies a na\u00efve Bayes classifier and Brier scores to interpolate untrained pattern information by using the trained ones probabilistically. Each Brier score is transformed into a more intuitive one, termed state prediction power (SPP). The SPP values represent the reliability of the system state prediction. A state prediction power map, which visualizes the DSVs and corresponding SPP values, is provided a more intuitive way of state prediction analysis. A case study using a car engine fault simulator was conducted to generate artificial engine knocking. The proposed method was evaluated using holdout cross-validation, defining specificity and sensitivity as indicators to represent state prediction success rates for no-fault and fault states, respectively. The results show that specificity and sensitivity are very high (equal to 1) for high limit values of SPP, but drop off dramatically for lower limit values.<\/jats:p>","DOI":"10.3390\/s20236839","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T10:26:12Z","timestamp":1606731972000},"page":"6839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Estimating System State through Similarity Analysis of Signal Patterns"],"prefix":"10.3390","volume":"20","author":[{"given":"Kichang","family":"Namgung","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunsik","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujeong","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Industrial Management Engineering, Hanbat National University, Daejeon 34158, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duck Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.mfglet.2013.09.005","article-title":"Recent advances and trends in predictive manufacturing systems in big data environment","volume":"1","author":"Lee","year":"2013","journal-title":"Manuf. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/0890-6955(94)90083-3","article-title":"A review by discussion of condition monitoring and fault diagnosis in machine tools","volume":"34","author":"Martin","year":"1994","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","article-title":"A review on machinery diagnostics and prognostics implementing condition-based maintenance","volume":"20","author":"Jardine","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.promfg.2019.02.060","article-title":"Model-based fault diagnosis and prognosis of dynamic systems: A review","volume":"30","author":"Ekanayake","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Estrada, F., Rotondo, D., and Valencia-Palomo, G. (2019). A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems. Processes, 7.","DOI":"10.3390\/pr7110814"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1007\/s00170-004-2174-8","article-title":"Novelty detection for practical pattern recognition in condition monitoring of multivariate processes: A case study","volume":"25","author":"Zorriassatine","year":"2005","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.comnet.2018.11.010","article-title":"Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection","volume":"148","author":"Salo","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7006","DOI":"10.1109\/TPEL.2015.2393373","article-title":"Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach","volume":"30","author":"Wang","year":"2015","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1504\/IJMIC.2019.102367","article-title":"Fault monitoring and diagnosis of aerostat actuator based on pca and state observer","volume":"32","author":"Zhang","year":"2019","journal-title":"Int. J. Model. Identif. Control"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/TII.2017.2670505","article-title":"A Manufacturing Big Data Solution for Active Preventive Maintenance","volume":"13","author":"Wan","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","article-title":"A review of process fault detection and diagnosis: Part III: Process history based methods","volume":"27","author":"Venkatasubramanian","year":"2003","journal-title":"Comput. Chem. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/34.824819","article-title":"Statistical pattern recognition: A review","volume":"22","author":"Jain","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1115\/1.1410933","article-title":"Structural Health Monitoring Using Statistical Pattern Recognition Techniques","volume":"123","author":"Sohn","year":"2001","journal-title":"J. Dyn. Syst. Meas. Control."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, H., and Pan, D. (2010). An improved piecewise aggregate approximation based on statistical features for time series mining. International Conference on Knowledge Science, Engineering and Management, Springer.","DOI":"10.1007\/978-3-642-15280-1_23"},{"key":"ref_15","unstructured":"Lkhagva, B., Suzuki, Y., and Kawagoe, K. (2006, January 1\u20133). Extended SAX: Extension of symbolic aggregate approximation for financial time series data representation. Proceedings of the 17th IEICE Data Engineering Workshop, Ginowan, Okinawa, Japan."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1063\/1.1531823","article-title":"A review of symbolic analysis of experimental data","volume":"74","author":"Daw","year":"2003","journal-title":"Rev. Sci. Instrum."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pensa, R.G., Leschi, C., Besson, J., and Boulicaut, J.-F. (2004, January 22). Assessment of Discretization Techniques for Relevant Pattern Discovery from Gene Expression Data. Proceedings of the 4th International Conference on Data Mining in Bioinformatics, Seattle, WA, USA.","DOI":"10.1007\/978-3-540-30214-8_18"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.ymssp.2005.08.022","article-title":"Symbolic time series analysis of ultrasonic data for early detection of fatigue damage","volume":"21","author":"Gupta","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1109\/TCYB.2016.2540657","article-title":"Empirical sensitivity analysis of discretization parameters for fault pattern extraction from multivariate time series data","volume":"47","author":"Baek","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"M\u00f6rchen, F., and Ultsch, A. (2005, January 21\u201324). Optimizing Time Series Discretization for Knowledge Discovery. Proceedings of the 11th International Conference on Knowledge Discovery in Data Mining, Chicago, IL, USA.","DOI":"10.1145\/1081870.1081953"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0888-3270(91)90027-3","article-title":"An approach to state recognition and knowledge-based diagnosis for engines","volume":"5","author":"Hong","year":"1991","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1023\/A:1016304305535","article-title":"Discretization: An Enabling Technique","volume":"6","author":"Liu","year":"2002","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_23","unstructured":"Tang, J., Alelyani, S., and Liu, H. (2014). Data Classification: Algorithms and Applications, CRC Press."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.1016\/j.asoc.2012.03.021","article-title":"A comparative study of Na\u00efve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis","volume":"12","author":"Muralidharan","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.epsr.2015.06.008","article-title":"A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors","volume":"127","author":"Goedtel","year":"2015","journal-title":"Electr. Power Syst. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.neucom.2017.08.035","article-title":"A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data","volume":"275","author":"Collell","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/0030-5073(82)90237-9","article-title":"External correspondence: Decompositions of the mean probability score","volume":"30","author":"Yates","year":"1982","journal-title":"Organ. Behav. Hum. Perform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","article-title":"Verification of forecasts expressed in terms of probability","volume":"78","author":"Brier","year":"1950","journal-title":"Mon. Weather Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1002\/met.21","article-title":"Performance targets and the Brier score","volume":"14","author":"Roulston","year":"2007","journal-title":"Meteorol. Appl."},{"key":"ref_30","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6839\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:39:24Z","timestamp":1760179164000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,30]]},"references-count":30,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236839"],"URL":"https:\/\/doi.org\/10.3390\/s20236839","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,11,30]]}}}