4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine †

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Diesel engine fault diagnosis is vital due to enhanced reliability and economic efficiency requirements. The extracted features in traditional fault diagnosis are constructed manually, which is very cumbersome because of the requirement for lots of expertise. To handle this issue, this paper proposed a variational stacked autoencoder (VSAE) to adaptively extract features from angular domain signals. As an unsupervised algorithm, VSAE can extract high-level features with the help of multiple encoding layers. Layer-wise pre-training and fine-tuning are introduced to get a better network initialization value. Moreover, the dropout technique and the batch normalization technique are carried out to prevent over-fitting and implement fast convergence. Finally, the harmony search optimizer (HSO) algorithm is introduced to get an appropriate hyper-parameter setting in the VSAE model, as well as make adaptive adjustment of the network structure. In order to verify the proposed method, the valve train fault data is collected on the diesel engine test rig under twelve operating conditions. The results indicate that the proposed scheme can effectively diagnose different degrees of intake valve fault, exhaust valve fault, and coupling fault under various operating conditions. Furthermore, the classification accuracy improved from 94.10% to 98.85%VSAE compared with stacked autoencoder (SAE) and some other traditional fault diagnosis algorithms.

          Related collections

          Most cited references27

          • Record: found
          • Abstract: not found
          • Article: not found

          A New Heuristic Optimization Algorithm: Harmony Search

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Visualizing data using ti-SNE

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Auto-Encoding Variational Bayes

              How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.
                Bookmark

                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                31 December 2019
                January 2020
                : 20
                : 1
                : 223
                Affiliations
                [1 ]Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education, Beijing University of Chemical Technology, Beijing 100029, China; chenkun_chn@ 123456163.com (K.C.); 2017400141@ 123456mail.buct.edu.cn (H.Z.)
                [2 ]Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China; jiangzn@ 123456mail.buct.edu.cn (Z.J.); zhangjinjie@ 123456mail.buct.edu.cn (J.Z.)
                Author notes
                [* ]Correspondence: maozhiwei@ 123456mail.buct.edu.cn ; Tel.: +86-176-0011-7869
                [†]

                This Paper is an Expanded Version of “Valve Fault Diagnosis of Internal Combustion Engine Based on An Improved Stacked Autoencoder” in the Proceedings of the SDPC 2019, Beijing, China, 15–17 August 2019.

                Author information
                https://orcid.org/0000-0001-5839-5066
                Article
                sensors-20-00223
                10.3390/s20010223
                6982694
                31906062
                fe31c6b6-08e7-4d35-bce3-9c697fd7e3ab
                © 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/).

                History
                : 21 November 2019
                : 29 December 2019
                Categories
                Article

                Biomedical engineering
                deep learning,autoencoder,harmony search optimizer,diesel engine,fault diagnosis

                Comments

                Comment on this article