Knowledge-Enabled Machine Learning for Predictive Diagnostics: A Case Study for an Automotive Diesel Particulate Filter

Doikin, Aleksandr; Campean, Felician; Neagu, Daniel; Priest, Martin; Soleimani, Morteza; Lin, Chunxing

doi:10.3850/978-981-14-8593-0_5573-cd

Cited by 3 publications

(4 citation statements)

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“…This integration adds some flexibility to the modeling process. 150,151 Table 3 compares different knowledge-based methods by summarizing their generic advantages and disadvantages.…”

Section: Discussionmentioning

confidence: 99%

“…However, knowledge‐based models are more associated with hybrid approaches rather than independent diagnostics and prognostics techniques in recent years. This integration adds some flexibility to the modeling process 150,151 . Table 3 compares different knowledge‐based methods by summarizing their generic advantages and disadvantages.…”

Section: Diagnostics and Prognostics Approachesmentioning

confidence: 99%

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Diagnostics and prognostics for complex systems: A review of methods and challenges

Soleimani

Campean

Neagu

2021

Quality & Reliability Eng

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Diagnostics and prognostics have a significant role in the reliability enhancement of systems and are focused topics of active research. Engineered systems are becoming more complex and are subjected to miscellaneous failure modes that impact adversely their performability. This ever‐increasing complexity makes fault diagnostics and prognostics challenging for the system‐level functions. A significant number of successes have been achieved and acknowledged in some review papers; however, these reviews rarely focused on application to complex engineered systems nor provided a systematic review of diverse techniques and approaches to address the related challenges. To bridge the gap, this paper first presents a review to systematically cover the general concepts and recent development of various diagnostics and prognostics approaches, along with their strengths and shortcomings for the application of diverse engineered systems. Afterwards, given the characteristics of complex systems, the applicability of different techniques and methods that are capable to address the features of complex systems are reviewed and discussed, and some of the recent achievements in the literature are introduced. Finally, the unaddressed challenges are discussed by taking into account the characteristics of automotive systems as an example of complex systems. In addition, future development and potential research trends are offered to address those challenges. Consequently, this review provides a systematic view of the state‐of‐the‐art and case studies with a reference value for scholars and practitioners.

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“…This integration adds some flexibility to the modeling process. 150,151 Table 3 compares different knowledge-based methods by summarizing their generic advantages and disadvantages.…”

Section: Discussionmentioning

confidence: 99%

Section: Diagnostics and Prognostics Approachesmentioning

confidence: 99%

Diagnostics and prognostics for complex systems: A review of methods and challenges

Soleimani

Campean

Neagu

2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…This reflects the partition of predictor variables into "journey parameters" and "driver behaviour". The second category of predictors capture the way in which the vehicle is drivenwhich is dependent on the driver choice or driver specific behaviour, for which a range of trip statistics can be used as data features for the machine learning model (see for example Doikin et al, 2020). The classification accuracy is then evaluated using the confusion matrix, which calculates the number of correctly classified classes versus the total number of attempts.…”

Section: Framework For Driver Behaviour Modellingmentioning

confidence: 99%

“…However, even in these cases, the robustness of the current health ICED21 state estimation is affected by sensor fidelity (Tamssaouet et al, 2020), where measurement noise can be caused by electrical interference, digitisation error, sensor bias, dead-band, backlash, and nonlinearity in the response (Saha and Goebel, 2008). In the previous work (Doikin et al, 2020), a knowledge-enabled data-driven framework was presented to increase the trust level to sensor measurements. The challenge presented in this paper focuses on prognostics modelling, which is affected by future uncertainties.…”

Section: Figure 2 Uncertainty Quantification In Prognostics Modellingmentioning

confidence: 99%

A Reliability Inspired Strategy for Intelligent Performance Management With Predictive Driver Behaviour: A Case Study for a Diesel Particulate Filter

et al. 2021

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The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.

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A Bayesian network development methodology for fault analysis; case study of the automotive aftertreatment system

Soleimani,

Shahbeigi,

Nasr Esfahani

2024

Mechanical Systems and Signal Processing

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Knowledge-Enabled Machine Learning for Predictive Diagnostics: A Case Study for an Automotive Diesel Particulate Filter

Cited by 3 publications

References 0 publications

Diagnostics and prognostics for complex systems: A review of methods and challenges

Diagnostics and prognostics for complex systems: A review of methods and challenges

A Reliability Inspired Strategy for Intelligent Performance Management With Predictive Driver Behaviour: A Case Study for a Diesel Particulate Filter

A Bayesian network development methodology for fault analysis; case study of the automotive aftertreatment system

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