CoChat
Sign Up
Cited by 581Reliability and Maintenance OptimizationMachine Fault Diagnosis TechniquesFault Detection and Control Systems

Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry

Open access
Theissler, Andreas, Pérez-Velázquez, Judith, Kettelgerdes, Marcel, Elger, Gordon·2021·Reliability Engineering & System Safety
DOI 10.1016/j.ress.2021.107864
Open this paper in CoChat — free accountOpen PDF

Abstract

Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ress.2021.107864

Cites

17 of 17 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 155

  1. Iung B, Levrat E, Marquez A Crespo, Erbe H. E-maintenance: principles, review and conceptual framework. IFAC Proc Vol 2007;40(19):18-29. http://dx.doi. org/10.3182/20071002-MX-4-3906.00005. · DOI
  2. Pecht MG, Kang M. Prognostics and health management of electronics: fun- damentals, machine learning, and the internet of things. Wiley -IEEE, Wiley; 2018.
  3. Cachada A, Barbosa J, Leitño P, Gcraldcs CAS, Deusdado L, Costa J, et al. Maintenance 4.0: Intelligent and predictive maintenance system architecture. In: 2018 IEEE 23rd international conference on emerging technologies and factory automation, vol. 1. 2018, p. 139-46. http://dx.doi.org/10.1109/ETFA. 2018.8502489. · DOI
  4. Bokrantz Jon, Skoogh Anders, Berlin Cecilia, Wuest Thorsten, Stahre Johan. Smart Maintenance: an empirically grounded conceptualization. Int J Prod Econ 2020;223:107534. http://dx.doi.org/10.1016/j.ijpe.2019.107534. · DOI
  5. Theissler Andreas. Detecting anomalies in multivariate time series from automotive systems [Ph.D. thesis], Brunel University London; 2013.
  6. Becker Jan, Helmle Michael, Pink Oliver. System architecture and safety requirements for automated driving. In: Automated driving: safer and more efficient future driving. Cham: Springer International Publishing; 2017, p. 265-83. http://dx.doi.org/10.1007/978-3-319-31895-0_11. · DOI
  7. Reschka Andreas. Safety concept for autonomous vehicles. In: Autonomous driving: technical, legal and social aspects. Berlin, Heidelberg: Springer Berlin Heidelberg; 2016, p. 473-96. http://dx.doi.org/10.1007/978-3-662-48847-8_ 23. · DOI
  8. Kim Junsung, Rajkumar Ragunathan (Raj), Jochim Markus. Towards depend- able autonomous driving vehicles: A system-level approach. SIGBED Rev. 2013;10(1):29-32. http://dx.doi.org/10.1145/2492385.2492390. · DOI
  9. Zhao Y, Liu P, Wang Z, Zhang L, Hong J. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Appl Energy 2017;207:354-62. http://dx.doi.org/10.1016/j.apenergy.2017.05.139. · DOI
  10. Nowakowski Tomasz, Tubis Agnieszka, Werbińska-Wojciechowska Sylwia. Evo- lution of technical systems maintenance approaches-review and a case study. In: International conference on intelligent systems in production engineering and maintenance. Springer; 2018, p. 161-74.
  11. Sutharssan Thamo, Stoyanov Stoyan, Bailey Chris, Yin Chunyan. Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms. J Eng 2015;2015(7):215-22.
  12. Peng Ying, Dong Ming, Zuo Ming. Current status of machine prognos- tics in condition-based maintenance: A review. Int J Adv Manuf Technol 2010;50:297-313. http://dx.doi.org/10.1007/s00170-009-2482-0. · DOI
  13. Werbińska-Wojciechowska Sylwia. Preventive maintenance models for technical systems. In: Technical system maintenance: delay-time-based modelling. Cham: Springer International Publishing; 2019, p. 21-100. http://dx.doi.org/10.1007/ 978-3-030-10788-8_2. · DOI
  14. Wu Shaomin. Preventive maintenance models: A review. In: Tadj Lotfi, Ouali M- Salah, Yacout Soumaya, Ait-Kadi Daoud, editors. Replacement models with minimal repair. London: Springer London; 2011, p. 129-40. http://dx.doi.org/ 10.1007/978-0-85729-215-5_4. · DOI
  15. Ali Yasir Hassan. Artificial intelligence application in machine condition moni- toring and fault diagnosis. In: Artificial intelligence. Rijeka: IntechOpen; 2018, http://dx.doi.org/10.5772/intechopen.74932. · DOI
  16. Alsina Emanuel F, Chica Manuel, Trawiński Krzysztof, Regattieri Alberto. On the use of machine learning methods to predict component reliabil- ity from data-driven industrial case studies. Int J Adv Manuf Technol 2018;94(5):2419-33.
  17. Carvalho Thyago P, Soares Fabrízzio AAMN, Vita Roberto, da P. Fran- cisco Roberto, Basto João P, Alcalá Symone GS. A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 2019;137:106024. http://dx.doi.org/10.1016/j.cie.2019.106024. · DOI
  18. Khan Samir, Yairi Takehisa. A review on the application of deep learning in system health management. Mech Syst Signal Process 2018;107:241-65. http://dx.doi.org/10.1016/j.ymssp.2017.11.024. · DOI · Paper page
  19. Schwabacher Mark, Goebel Kai. A survey of artificial intelligence for prognos- tics. In: AAAI fall symposium: artificial intelligence for prognostics. 2007, p. 108-15.
  20. Tsui Kwok-Leung, Chen Nan, Zhou Qiang, Hai Yizhen, Wang Wenbin. Prognos- tics and health management: A review on data driven approaches. Math Probl Eng 2015;2015:1-17.
  21. Wu Shaomin, Tsui Kwok L, Chen Nan, Zhou Qiang, Hai Yizhen, Wang Wenbin. Prognostics and health management: A review on data driven approaches. Math Probl Eng 2015;2015:793161.
  22. Wu Shaomin, Wu D, Peng R. Machine learning approaches in reliability and maintenance: classifications of recent literature. In: 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling. 2020, p. 1-5.
  23. Zhao Rui, Yan Ruqiang, Chen Zhenghua, Mao Kezhi, Wang Peng, Gao Robert X. Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 2019;115:213-37. http://dx.doi.org/10.1016/j.ymssp.2018.05. 050. · DOI · Paper page
  24. Ahsan M, Stoyanov S, Bailey C. Prognostics of automotive electronics with data driven approach: A review. In: 39th international spring seminar on electronics technology. 2016, p. 279-84. http://dx.doi.org/10.1109/ISSE.2016.7563205. · DOI
  25. Mesgarpour Mohammad, Landa-Silva Dario, Dickinson Ian. Overview of telematics-based prognostics and health management systems for commercial vehicles. In: Mikulski Jerzy, editor. Activities of transport telematics. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013, p. 123-30.
  26. Sankavaram C, Pattipati B, Kodali A, Pattipati K, Azam M, Kumar S, et al. Model-based and data-driven prognosis of automotive and electronic systems. In: 2009 IEEE international conference on automation science and engineering. 2009, p. 96-101. http://dx.doi.org/10.1109/COASE.2009.5234108. · DOI
  27. Li Jun, Cheng Hong, Guo Hongliang, Qiu Shaobo. Survey on artificial intelligence for vehicles. Autom Innov 2018;1(1):2-14.
  28. Falcini F, Lami G, Costanza A. Deep learning in automotive software. IEEE Softw 2017;34(03):56-63. http://dx.doi.org/10.1109/MS.2017.79. · DOI
  29. Zhang Weiting, Yang Dong, Wang Hongchao. Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst J 2019;13(3):2213-27.
  30. Yan Rundong, Dunnett Sarah J, Jackson Lisa M. Novel methodology for optimising the design, operation and maintenance of a multi-AGV system. Reliab Eng Syst Saf 2018;178:130-9.
  31. Li X, Ding Q, Sun J-Q. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf 2018;172:1-11. http: //dx.doi.org/10.1016/j.ress.2017.11.021. · DOI
  32. Li X, Zhang W, Ding Q. Deep learning-based remaining useful life estima- tion of bearings using multi-scale feature extraction. Reliab Eng Syst Saf 2019;182:208-18. http://dx.doi.org/10.1016/j.ress.2018.11.011. · DOI
  33. Chen Chong, Liu Ying, Sun Xianfang, Di Cairano-Gilfedder Carla, Titmus Scott. Automobile maintenance modelling using gcforest. In: 2020 IEEE 16th inter- national conference on automation science and engineering. IEEE; 2020, p. 600-5.
  34. Russell Stuart, Norvig Peter. Artificial intelligence: A modern approach. 3rd ed. USA: Prentice Hall Press; 2009.
  35. Rocchetta R, Bellani L, Compare M, Zio E, Patelli E. A reinforcement learning framework for optimal operation and maintenance of power grids. Appl Energy 2019;241:291-301. http://dx.doi.org/10.1016/j.apenergy.2019.03.027. · DOI
  36. Zhang N, Si W. Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks. Reliab Eng Syst Saf 2020;203. http://dx.doi.org/10.1016/j.ress.2020.107094. · DOI
  37. Arulkumaran Kai, Deisenroth Marc Peter, Brundage Miles, Bharath Anil An- thony. Deep reinforcement learning: A brief survey. IEEE Signal Process Mag 2017;34(6):26-38.
  38. Koprinkova-Hristova P. Reinforcement learning for predictive maintenance of industrial plants. Inf Technol Control 2013;11(1):21-8.
  39. Ong Kevin Shen Hoong, Niyato Dusit, Yuen Chau. Predictive maintenance for edge-based sensor networks: A deep reinforcement learning approach. In: 2020 IEEE 6th world forum on internet of things. IEEE; 2020, p. 1-6.
  40. Bezdek James C, Ehrlich Robert, Full William. FCM: The fuzzy c-means clustering algorithm. Comput Geosci 1984;10(2-3):191-203.
  41. Ester Martin, Kriegel Hans-Peter, Sander Jörg, Xu Xiaowei, et al. A density- based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, vol. 96. 1996, p. 226-31.
  42. Abe Shigeo. Support vector machines for pattern classification (advances in pattern recognition). 2nd ed. Springer-Verlag London Ltd.; 2010.
  43. Tamersoy Acar, Khalil Elias, Xie Bo, Lenkey Stephen L, Routledge Bryan R, Chau Duen Horng, Navathe Shamkant B. Large-scale insider trading analysis: patterns and discoveries. Soc Netw Anal Min 2014;4(1):201.
  44. Garcia-Teodoro Pedro, Diaz-Verdejo Jesus, Maciá-Fernández Gabriel, Vázquez Enrique. Anomaly-based network intrusion detection: Techniques, systems and challenges. Comput Secur 2009;28(1-2):18-28.
  45. Avatefipour Omid, Al-Sumaiti Ameena Saad, El-Sherbeeny Ahmed M, Awwad Emad Mahrous, Elmeligy Mohammed A, Mohamed Mohamed A, et al. An intelligent secured framework for cyberattack detection in electric vehicles' CAN bus using machine learning. IEEE Access 2019;7:127580-92.
  46. Prisacaru Alexandru, Palczynska Alicja, Theissler Andreas, Gromala Przemys- law, Han Bongtae, Zhang GQ. In situ failure detection of electronic control units using piezoresistive stress sensor. IEEE Trans Compon Packag Manuf Technol 2018;PP:1-14.
  47. Theissler Andreas, Frey Stephan, Ehlert Jens. OCADaMi: One-class anomaly detection and data mining toolbox. In: Joint European conference on machine learning and knowledge discovery in databases 2019. Springer; 2020, p. 764-8.
  48. Tax David, Duin Robert. Support vector data description. Mach Learn 2004;54(1):45-66.
  49. Schölkopf Bernhard, Platt John C, Shawe-Taylor John C, Smola Alex J, Williamson Robert C. Estimating the support of a high-dimensional distribution. Neural Comput 2001;13:1443-71.
  50. Chandola Varun, Banerjee Arindam, Kumar Vipin. Anomaly detection: A survey. ACM Comput Surv 2009;41(3):15:1-58.
  51. Theissler Andreas. Multi-class novelty detection in diagnostic trouble codes from repair shops. In: 2017 IEEE 15th international conference on industrial informatics. IEEE; 2017, p. 1043-9. http://dx.doi.org/10.1109/INDIN.2017. 8104917. · DOI
  52. Rai R, Sahu CK. Driven by data or derived through physics? A review of hybrid physics guided machine learning techniques with cyber-physical system (CPS) focus. IEEE Access 2020;8:71050-73. http://dx.doi.org/10.1109/ACCESS.2020. 2987324. · DOI
  53. He Zengyou, Xu Xiaofei, Deng Shengchun. Discovering cluster-based local outliers. Pattern Recognit Lett 2003;24(9-10):1641-50. http://dx.doi.org/10. 1016/S0167-8655(03)00003-5. · DOI
  54. Hariri S, Carrasco Kind M, Brunner RJ. Extended isolation forest. IEEE Trans Knowl Data Eng 2019.
  55. Zhou Chong, Paffenroth Randy C. Anomaly detection with robust deep autoen- coders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. New York, NY, USA: Association for Computing Machinery; 2017, p. 665-74. http://dx.doi.org/10.1145/3097983. 3098052. · DOI
  56. Cao VL, Nicolau M, McDermott J. Learning neural representations for network anomaly detection. IEEE Trans Cybern 2019;49(8):3074-87.
  57. Ruff Lukas, Vandermeulen Robert, Goernitz Nico, Deecke Lucas, Sid- diqui Shoaib Ahmed, Binder Alexander, et al. Deep one-class classification. In: Proceedings of ICML, vol. 80. Stockholmsmässan, Stockholm Sweden: PMLR; 2018, p. 4393-402.
  58. Sun Jiayu, Shao Jie, He Chengkun. Abnormal event detection for video surveillance using deep one-class learning. Multimedia Tools Appl 2019;78(3):3633-47. http://dx.doi.org/10.1007/s11042-017-5244-2. · DOI
  59. Maaten Laurens van der, Hinton Geoffrey. Visualizing data using t-SNE. J Mach Learn Res 2008;9(86):2579-605.
  60. Akçay Samet, Atapour-Abarghouei Amir, Breckon Toby P. GANomaly: Semi- supervised anomaly detection via adversarial training. In: Computer vision. Cham: Springer International Publishing; 2019, p. 622-37.
  61. Akçay Samet, Atapour-Abarghouei Amir, Breckon Toby P. Skip-GANomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In: 2019 international joint conference on neural networks. 2019, p. 1-8.
  62. Wang X, Shen C, Xia M, Wang D, Zhu J, Zhu Z. Multi-scale deep intra-class transfer learning for bearing fault diagnosis. Reliab Eng Syst Saf 2020;202. http://dx.doi.org/10.1016/j.ress.2020.107050. · DOI
  63. Wei Dongdong, Han Te, Chu Fulei, Zuo Ming Jian. Weighted domain adap- tation networks for machinery fault diagnosis. Mech Syst Signal Process 2021;158:107744. http://dx.doi.org/10.1016/j.ymssp.2021.107744. · DOI
  64. da Costa PRDO, Akçay A, Zhang Y, Kaymak U. Remaining useful lifetime prediction via deep domain adaptation. Reliab Eng Syst Saf 2020;195. http: //dx.doi.org/10.1016/j.ress.2019.106682. · DOI
  65. Phillips J, Cripps E, Lau JW, Hodkiewicz MR. Classifying machinery condition using oil samples and binary logistic regression. Mech Syst Signal Process 2015;60:316-25. http://dx.doi.org/10.1016/j.ymssp.2014.12.020. · DOI
  66. Sankavaram C, Kodali A, Pattipati KR, Singh S. Incremental classifiers for data-driven fault diagnosis applied to automotive systems. IEEE Access 2015;3:407-19. http://dx.doi.org/10.1109/ACCESS.2015.2422833. · DOI
  67. Byttner S, Rögnvaldsson T, Svensson M. Consensus self-organized models for fault detection (COSMO). Eng Appl Artif Intell 2011;24(5):833-9. http://dx. doi.org/10.1016/j.engappai.2011.03.002. · DOI
  68. Fan Y, Nowaczyk S, Rögnvaldsson T. Incorporating expert knowledge into a self- organized approach for predicting compressor faults in a city bus fleet. Front Artif Intell Appl 2015;278:58-67. http://dx.doi.org/10.3233/978-1-61499-589- 0-58. · DOI
  69. Killeen Patrick, Ding Bo, Kiringa Iluju, Yeap Tet. IoT-Based predictive main- tenance for fleet management. Procedia Comput Sci 2019;151:607-13. http: //dx.doi.org/10.1016/j.procs.2019.04.184, The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019) / The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019) / Affiliated Workshops. · DOI
  70. Khoshkangini Reza, Mashhadi Peyman Sheikholharam, Berck Peter, Gho- lami Shahbandi Saeed, Pashami Sepideh, Nowaczyk Sławomir, et al. Early prediction of quality issues in automotive modern industry. Information 2020;11(7):354.
  71. Wong PK, Zhong J, Yang Z, Vong CM. Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis. Neurocomputing 2016;174:331-43. http://dx.doi.org/10.1016/j.neucom.2015.02.097. · DOI
  72. Zhong J-H, Wong PK, Yang Z-X. Fault diagnosis of rotating machinery based on multiple probabilistic classifiers. Mech Syst Signal Process 2018;108:99-114. http://dx.doi.org/10.1016/j.ymssp.2018.02.009. · DOI
  73. Wang M-H, Chao K-H, Sung W-T, Huang G-J. Using ENN-1 for fault recognition of automotive engine. Expert Syst Appl 2010;37(4):2943-7. http://dx.doi.org/ 10.1016/j.eswa.2009.09.041. · DOI
  74. Zabihihesari Alireza, Ansari-Rad Saeed, Shirazi Farzad, Ayati Moosa. Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network. Proc Inst Mech Eng C 2018;233:095440621877831. http://dx.doi.org/10.1177/0954406218778313. · DOI
  75. Jung D, Sundström C. A combined data-driven and model-based residual selection algorithm for fault detection and isolation. IEEE Trans Control Syst Technol 2019;27(2):616-30. http://dx.doi.org/10.1109/TCST.2017.2773514. · DOI
  76. Wolf P, Mrowca A, Nguyen TT, Baker B, Gunnemann S. Pre-ignition detection using deep neural networks: A step towards data-driven automotive diagnostics. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, vol. 2018-November. 2018, p. 176-83. http://dx.doi.org/10.1109/ITSC.2018. 8569908. · DOI
  77. Jung Daniel. Data-driven open-set fault classification of residual data using Bayesian filtering. IEEE Trans Control Syst Technol 2020;28(5):2045-52.
  78. Wang YS, Liu NN, Guo H, Wang XL. An engine-fault-diagnosis system based on sound intensity analysis and wavelet packet pre-processing neural network. Eng Appl Artif Intell 2020;94:103765.
  79. Pan H, Lü Z, Wang H, Wei H, Chen L. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine. Energy 2018;160:466-77. http://dx.doi.org/10.1016/j.energy.2018. 06.220. · DOI · Paper page
  80. You G, Park S, Oh D. Diagnosis of electric vehicle batteries using recurrent neural networks. IEEE Trans Ind Electron 2017;64(6):4885-93. http://dx.doi. org/10.1109/TIE.2017.2674593. · DOI
  81. Yang R, Xiong R, He H, Chen Z. A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles applica- tion. J Cleaner Prod 2018;187:950-9. http://dx.doi.org/10.1016/j.jclepro.2018. 03.259. · DOI
  82. Quintián H, Casteleiro-Roca J-L, Perez-Castelo FJ, Calvo-Rolle JL, Corchado E. Hybrid intelligent model for fault detection of a Lithium Iron Phosphate power cell used in electric vehicles. Lect Notes Comput Sci 2016;9648:751-62. http: //dx.doi.org/10.1007/978-3-319-32034-2_63. · DOI
  83. Sankavaram C, Pattipati B, Pattipati K, Zhang Y, Howell M, Salman M. Data- driven fault diagnosis in a hybrid electric vehicle regenerative braking system. In: IEEE aerospace conference proceedings. 2012, http://dx.doi.org/10.1109/ AERO.2012.6187368. · DOI
  84. Theissler A. Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl-Based Syst 2017;123:163-73. http: //dx.doi.org/10.1016/j.knosys.2017.02.023. · DOI
  85. Shafi U, Safi A, Shahid AR, Ziauddin S, Saleem MQ. Vehicle remote health monitoring and prognostic maintenance system. J Adv Transp 2018;2018. http://dx.doi.org/10.1155/2018/8061514. · DOI
  86. Tagawa T, Tadokoro Y, Yairi T. Structured denoising autoencoder for fault detection and analysis. J Mach Learn Res 2014;39(2014):96-111.
  87. Routray A, Rajaguru A, Singh S. Data reduction and clustering techniques for fault detection and diagnosis in automotives. In: 2010 IEEE international conference on automation science and engineering. 2010, p. 326-31. http: //dx.doi.org/10.1109/COASE.2010.5584324. · DOI
  88. Rengasamy D, Jafari M, Rothwell B, Chen X, Figueredo GP. Deep learning with dynamically weighted loss function for sensor-based prognostics and health management. Sensors (Switzerland) 2020;20(3). http://dx.doi.org/10. 3390/s20030723. · DOI · Paper page
  89. Cerqueira V, Pinto F, Sá C, Soares C. Combining boosted trees with metafeature engineering for predictive maintenance. Lect Notes Comput Sci 2016;9897 LNCS:393-7. http://dx.doi.org/10.1007/978-3-319-46349-0_35. · DOI
  90. Nowaczyk S, Prytz R, Rögnvaldsson T, Byttner S. Towards a machine learning algorithm for predicting truck compressor failures using logged vehicle data. Front Artif Intell Appl 2013;257:205-14. http://dx.doi.org/10.3233/978-1- 61499-330-8-205. · DOI
  91. Heidari Bafroui H, Ohadi A. Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing 2014;133:437-45. http://dx.doi.org/10.1016/j.neucom.2013. 12.018. · DOI
  92. Gharavian MH, Almas Ganj F, Ohadi AR, Heidari Bafroui H. Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gear- boxes. Neurocomputing 2013;121:150-9. http://dx.doi.org/10.1016/j.neucom. 2013.04.033. · DOI · Paper page
  93. Zhang T, Li Z, Deng Z, Hu B. Hybrid data fusion DBN for intelligent fault diagnosis of vehicle reducers. Sensors (Switzerland) 2019;19(11). http://dx.doi. org/10.3390/s19112504. · DOI
  94. Yin S, Huang Z. Performance monitoring for vehicle suspension system via fuzzy positivistic C-means clustering based on accelerometer measure- ments. IEEE/ASME Trans Mechatronics 2015;20(5):2613-20. http://dx.doi.org/ 10.1109/TMECH.2014.2358674. · DOI
  95. Wang G, Yin S. Data-driven fault diagnosis for an automobile suspension system by using a clustering based method. J Franklin Inst B 2014;351(6):3231-44. http://dx.doi.org/10.1016/j.jfranklin.2014.03.004. · DOI
  96. Zehelein Thomas, Hemmert-Pottmann Thomas, Lienkamp Markus. Diagnosing automotive damper defects using convolutional neural networks and electronic stability control sensor signals. J Sensor Actuator Netw 2020;9:8. http://dx.doi. org/10.3390/jsan9010008. · DOI
  97. Capriglione D, Carratù M, Liguori C, Paciello V, Sommella P. A soft stroke sensor for motorcycle rear suspension. Measurement 2017;106:46-52.
  98. Capriglione Domenico, Carratù Marco, Pietrosanto Antonio, Sommella Paolo. NARX ANN-Based instrument fault detection in motorcycle. Measurement 2018;117:304-11.
  99. Jeong Kicheol, Choi Seibum B, Choi Hyungjeen. Sensor fault detection and isolation using a support vector machine for vehicle suspension systems. IEEE Trans Veh Technol 2020;69(4):3852-63.
  100. Jegadeeshwaran R, Sugumaran V. Brake fault diagnosis using clonal selection classification algorithm (CSCA) -A statistical learning approach. Eng Sci Technol Int J 2015;18(1):14-23. http://dx.doi.org/10.1016/j.jestch.2014.08. 001. · DOI
  101. Alamelu Manghai TM, Jegadeeshwaran R. Vibration based brake health moni- toring using wavelet features: A machine learning approach. JVC/J Vib Control 2019;25(18):2534-50. http://dx.doi.org/10.1177/1077546319859704. · DOI
  102. Ghimire R, Sankavaram C, Ghahari A, Pattipati K, Ghoneim Y, Howell M, et al. Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system. In: AUTOTEST- CON (proceedings). 2011, p. 70-7. http://dx.doi.org/10.1109/AUTEST.2011. 6058760. · DOI
  103. Ghimire R, Zhang C, Pattipati KR. A rough set-theory-based fault-diagnosis method for an electric power-steering system. IEEE/ASME Trans Mechatronics 2018;23(5):2042-53. http://dx.doi.org/10.1109/TMECH.2018.2863119. · DOI
  104. Fang Y, Cheng C, Dong Z, Min H, Zhao X. A fault diagnosis framework for autonomous vehicles based on hybrid data analysis methods combined with fuzzy PID control. In: 2020 3rd international conference on unmanned systems. 2020, p. 281-6. http://dx.doi.org/10.1109/ICUS50048.2020.9274856. · DOI
  105. Siegel Joshua E, Bhattacharyya Rahul, Sarma Sanjay, Deshpande Ajay. Smartphone-based wheel imbalance detection. In: ASME 2015 dynamic systems and control conference. American Society of Mechanical Engineers Digital Collection; 2015, http://dx.doi.org/10.1115/DSCC2015-9716. · DOI
  106. Siegel Joshua E, Sun Yongbin, Sarma Sanjay. Automotive diagnostics as a ser- vice: An artificially intelligent mobile application for tire condition assessment. In: Aiello Marco, Yang Yujiu, Zou Yuexian, Zhang Liang-Jie, editors. Artificial intelligence and mobile services. Cham: Springer International Publishing; 2018, p. 172-84.
  107. Mohammadi A, Djerdir A, Steiner NY, Bouquain D, Khaburi D. Diagnosis of PEMFC for automotive application. In: Proceedings: 2015 5th interna- tional youth conference on energy. 2015, http://dx.doi.org/10.1109/IYCE.2015. 7180793. · DOI
  108. Zuo Jian, Lv Hong, Zhou Daming, Xue Qiong, Jin Liming, Zhou Wei, et al. Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application. Appl Energy 2021;281:115937. http://dx.doi. org/10.1016/j.apenergy.2020.115937. · DOI
  109. Wu J-D, Kuo J-M. Fault conditions classification of automotive gener- ator using an adaptive neuro-fuzzy inference system. Expert Syst Appl 2010;37(12):7901-7. http://dx.doi.org/10.1016/j.eswa.2010.04.046. · DOI
  110. Peters B, Yildirim M, Gebraeel N, Paynabar K. Severity-based diagnosis for vehicular electric systems with multiple, interacting fault modes. Reliab Eng Syst Saf 2020;195. http://dx.doi.org/10.1016/j.ress.2019.106605. · DOI
  111. Şimşir Mehmet, Bayir Raif, Uyaroğlu Yılmaz. Real-time monitoring and fault diagnosis of a low power hub motor using feedforward neural network. Comput Intell Neurosci 2016;2016:1-13. http://dx.doi.org/10.1155/2016/7129376. · DOI
  112. Seera Manjeevan, Lim Chee Peng, Nahavandi Saeid, Loo Chu Kiong. Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models. Expert Syst Appl 2014;41(10):4891-903. http: //dx.doi.org/10.1016/j.eswa.2014.02.028. · DOI
  113. Jeong YiNa, Son SuRak, Jeong EunHee, Lee ByungKwan. An integrated self- diagnosis system for an autonomous vehicle based on an IoT gateway and deep learning. Appl Sci 2018;8:1164. http://dx.doi.org/10.3390/app8071164. · DOI
  114. Van Wyk F, Wang Y, Khojandi A, Masoud N. Real-time sensor anomaly detection and identification in automated vehicles. IEEE Trans Intell Transp Syst 2020;21(3):1264-76. http://dx.doi.org/10.1109/TITS.2019.2906038. · DOI
  115. Rezvani Mohammad, AbuAli Mohamed, Lee Seungchul, Lee Jay, Ni Jun. A comparative analysis of techniques for electric vehicle battery prognostics and health management (PHM). SAE Tech Pap 2011. http://dx.doi.org/10.4271/ 2011-01-2247. · DOI
  116. M, Sinaiski A, Subramania HS. Condition-based maintenance with multi- target classification models. New Gener Comput 2011;29(3):245-60. http://dx. doi.org/10.1007/s00354-010-0301-7. · DOI
  117. Wu Y, Xue Q, Shen J, Lei Z, Chen Z, Liu Y. State of health estimation for lithium-ion batteries based on healthy features and long short-term memory. IEEE Access 2020;8:28533-47.
  118. Wang Zhi-Hao, Hendrick, Horng Gwo-Jiun, Wu Hsin-Te, Jong Gwo-Jia. A prediction method for voltage and lifetime of lead-acid battery by using machine learning. Energy Explor Exploit 2020;38(1):310-29. http://dx.doi.org/ 10.1177/0144598719881223. · DOI
  119. Prytz Rune, Nowaczyk Sławomir, Rögnvaldsson Thorsteinn, Byttner Stefan. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng Appl Artif Intell 2015;41:139-50.
  120. Taie MA, Diab M, Elhelw M. Remote prognosis, diagnosis and maintenance for automotive architecture based on least squares support vector machine and multiple classifiers. In: International congress on ultra modern telecommunica- tions and control systems and workshops. 2012, p. 128-34. http://dx.doi.org/ 10.1109/ICUMT.2012.6459652. · DOI
  121. Lee C-Y, Huang T-S, Liu M-K, Lan C-Y. Data science for vibration heteroscedas- ticity and predictive maintenance of rotary bearings. Energies 2019;12(5). http://dx.doi.org/10.3390/en12050801. · DOI
  122. Al-Dahidi S, Di Maio F, Baraldi P, Zio E. Remaining useful life estimation in heterogeneous fleets working under variable operating conditions. Reliab Eng Syst Saf 2016;156:109-24. http://dx.doi.org/10.1016/j.ress.2016.07.019. · DOI
  123. Wee Bert Van, Banister David. How to write a literature review paper? Transp Rev 2016;36(2):278-88.
  124. Breunig Markus M, Kriegel Hans-Peter, Ng Raymond T, Sander Jörg. LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data. 2000, p. 93-104.
  125. Zhou Zhi-Hua, Feng Ji. Deep forest. Natl Sci Rev 2019;6(1):74-86.
  126. Liu Li, Chen Jie, Fieguth Paul, Zhao Guoying, Chellappa Rama, Pietikäi- nen Matti. From BoW to CNN: Two decades of texture representation for texture classification. Int J Comput Vis 2019;127(1):74-109.
  127. Deng Jia, Dong Wei, Socher Richard, Li Li-Jia, Li Kai, Fei-Fei Li. Imagenet: A large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. Ieee; 2009, p. 248-55.
  128. Bernard Jürgen, Zeppelzauer Matthias, Sedlmair Michael, Aigner Wolf- gang. VIAL: a unified process for visual interactive labeling. Vis Comput 2018;34(9):1189-207. http://dx.doi.org/10.1007/s00371-018-1500-3. · DOI
  129. Bernard Jurgen, Hutter Marco, Zeppelzauer Matthias, Fellner Dieter, Sedl- mair Michael. Comparing visual-interactive labeling with active learning: An experimental study. IEEE Trans Vis Comput Graphics 2018;24(1):298-308. http://dx.doi.org/10.1109/tvcg.2017.2744818. · DOI
  130. Grimmeisen Benedikt, Theissler Andreas. The machine learning model as a guide: Pointing users to interesting instances for labeling through visual cues. In: The 13th international symposium on visual information communication and interaction. ACM; 2020, http://dx.doi.org/10.1145/3430036.3430058. · DOI
  131. Beil David, Theissler Andreas. Cluster-clean-label: An interactive machine learn- ing approach for labeling high-dimensional data. In: The 13th international symposium on visual information communication and interaction. ACM; 2020, http://dx.doi.org/10.1145/3430036.3430060. · DOI
  132. Fu Yifan, Zhu Xingquan, Li Bin. A survey on instance selection for active learn- ing. Knowl Inf Syst 2012;35(2):249-83. http://dx.doi.org/10.1007/s10115-012- 0507-8. · DOI
  133. Theissler Andreas, Kraft Anna-Lena, Rudeck Max, Erlenbusch Fabian. VIAL- AD: Visual interactive labelling for anomaly detection -An approach and open research questions. In: 4th international workshop on interactive adaptive learning. CEUR-WS; 2020.
  134. Shorten Connor, Khoshgoftaar Taghi M. A survey on image data augmentation for deep learning. J Big Data 2019;6(1):60.
  135. Chawla Nitesh V, Bowyer Kevin W, Hall Lawrence O, Kegelmeyer W Philip. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002;16:321-57.
  136. Lu Jie, Liu Anjin, Dong Fan, Gu Feng, Gama Joao, Zhang Guangquan. Learning under concept drift: A review. IEEE Trans Knowl Data Eng 2018;31(12):2346-63.
  137. Webb Geoffrey I, Hyde Roy, Cao Hong, Nguyen Hai Long, Petitjean Francois. Characterizing concept drift. Data Min Knowl Discov 2016;30(4):964-94.
  138. Senge Robin, Bösner Stefan, Dembczyński Krzysztof, Haasenritter Jörg, Hirsch Oliver, Donner-Banzhoff Norbert, et al. Reliable classification: Learn- ing classifiers that distinguish aleatoric and epistemic uncertainty. Inf Sci 2014;255:16-29.
  139. Yang Qiang, Liu Yang, Chen Tianjian, Tong Yongxin. Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol (TIST) 2019;10(2):1-19.
  140. Ribeiro Marco Tulio, Singh Sameer, Guestrin Carlos. ''Why should I trust you? ''Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, p. 1135-44.
  141. Samek Wojciech, Montavon Grégoire, Vedaldi Andrea, Hansen Lars Kai, Müller Klaus-Robert. Explainable AI: interpreting, explaining and visualizing deep learning, vol. 11700. Springer Nature; 2019.
  142. Guidotti Riccardo, Monreale Anna, Ruggieri Salvatore, Turini Franco, Gian- notti Fosca, Pedreschi Dino. A survey of methods for explaining black box models. ACM Comput Surv (CSUR) 2018;51(5):1-42.
  143. Rudin Cynthia. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 2019;1(5):206-15.
  144. Gambs Sébastien, Killijian Marc-Olivier, del Prado Cortez Miguel Núñez. De- anonymization attack on geolocated data. J Comput Syst Sci 2014;80(8):1597- 614.
  145. Akhtar Naveed, Mian Ajmal. Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access 2018;6:14410-30.
  146. Cao Yulong, Xiao Chaowei, Cyr Benjamin, Zhou Yimeng, Park Won, Ram- pazzi Sara, et al. Adversarial sensor attack on lidar-based perception in autonomous driving. In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security. 2019, p. 2267-81.
  147. Strauss Thilo, Hanselmann Markus, Junginger Andrej, Ulmer Holger. Ensemble methods as a defense to adversarial perturbations against deep neural networks. 2017, ArXiv Preprint ArXiv:1709.03423. · arXiv
  148. Sargolzaei A, Crane CD, Abbaspour A, Noei S. A machine learning approach for fault detection in vehicular cyber-physical systems. In: 2016 15th IEEE in- ternational conference on machine learning and applications. 2016, p. 636-40. http://dx.doi.org/10.1109/ICMLA.2016.0112. · DOI
  149. Grüner Tobias, Böllhoff Falco, Meisetschläger Robert, Vydrenko Alexander, Bator Martyna, Dicks Alexander, et al. Evaluation of machine learning for sensorless detection and classification of faults in electromechanical drive systems. Procedia Comput Sci 2020;176:1586-95.
  150. Ding Yu, Ma Liang, Ma Jian, Suo Mingliang, Tao Laifa, Cheng Yujie, et al. Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach. Adv Eng Inf 2019;42:100977. http://dx.doi.org/10.1016/j.aei.2019.100977. · DOI
  151. LeCun Yann, Bengio Yoshua. The handbook of brain theory and neural networks. Cambridge, MA, USA: MIT Press; 1998, p. 255-8,
  152. Sak Hasim, Senior Andrew W, Beaufays Francoise. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: Interspeech. 2014.
  153. Malhotra Pankaj, Vig Lovekesh, Shroff Gautam, Agarwal Puneet. Long short term memory networks for anomaly detection in time series. In: Proceedings, vol. 89. Presses universitaires de Louvain; 2015, p. 89-94.
  154. Karim Fazle, Majumdar Somshubra, Darabi Houshang, Chen Shun. LSTM Fully convolutional networks for time series classification. IEEE Access 2017;6:1662-9.
  155. Dempster Angus, Petitjean François, Webb Geoffrey I. ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Min Knowl Discov 2020;34(5):1454-95.

Cited by

Sorted by citations

Authors
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G.
Affiliations
  • Hochschule Aalen (DE)
  • Technical University of Munich (DE)
  • Fraunhofer Institute for Transportation and Infrastructure Systems (DE)
Corresponding author
Pérez-Velázquez, Judith
ORCID
Venue
Reliability Engineering & System Safety
Publisher
Elsevier BV
Published
2021-11
Volume
215
Pages
107864
ISSN
0951-8320 (ISSN-L)
DOI
10.1016/j.ress.2021.107864
OpenAlex
W3174959864
Cited by
581
References
17
Citations by year
2026: 118 · 2025: 162 · 2024: 135 · 2023: 108 · 2022: 49 · 2021: 9
FWCI
54.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Reliability Engineering & System Safety (publishedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
  • mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
  • OPUS (Aalen University) (submittedVersion)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords
predictive maintenance, machine learning, automotive systems, vehicle maintenance, functional safety, maintenance cost reduction, supervised learning, labelled data, deep learning, interpretability, multi-source data fusion, open problems

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review