{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:06:41Z","timestamp":1771956401007,"version":"3.50.1"},"reference-count":16,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge \u201crun to failure\u201d and \u201crun to prior failure\u201d data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the \u201cpath to be followed\u201d for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA\u2019s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.<\/jats:p>","DOI":"10.3390\/s21248420","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T21:32:40Z","timestamp":1639690360000},"page":"8420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data"],"prefix":"10.3390","volume":"21","author":[{"given":"Muhammad Mohsin","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Advanced Design and Systems Engineering, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6796-7617","authenticated-orcid":false,"given":"Peter W.","family":"Tse","sequence":"additional","affiliation":[{"name":"Department of Advanced Design and Systems Engineering, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-7012","authenticated-orcid":false,"given":"Amy J. C.","family":"Trappey","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"045607","DOI":"10.1088\/0957-0233\/23\/4\/045607","article-title":"Generating an indicator for pump impeller damage using half and full spectra, fuzzy preference-based rough sets and PCA","volume":"23","author":"Zhao","year":"2012","journal-title":"Meas. Sci. Technol."},{"key":"ref_2","unstructured":"Jinfei, H. (2014). 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