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      A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions

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          Abstract

          The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions.

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          Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences

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              EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                06 June 2019
                June 2019
                : 19
                : 11
                Affiliations
                [1 ]Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China; 2017400141@ 123456mail.buct.edu.cn (H.Z.); zhangjinjie@ 123456mail.buct.edu.cn (J.Z.); jiangzn@ 123456mail.buct.edu.cn (Z.J.)
                [2 ]Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China; 2016200682@ 123456mail.buct.edu.cn (D.W.); 2018200649@ 123456mail.buct.edu.cn (X.Z.)
                Author notes
                [* ]Correspondence: maozhiwei@ 123456mail.buct.edu.cn ; Tel.: +86-1760-011-7869
                Article
                sensors-19-02590
                10.3390/s19112590
                6603593
                31174383
                43d73e1c-64ed-4164-b66f-b8ecbb3c7a4e
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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