A Comparative Study of Deep Learning-Based Diagnostics for Automotive Safety Components Using a Raspberry Pi

Lee, Namkyoung; Azarian, Michael H.; Pecht, Michael; Kim, Jinyong; Im, Jongsoon

doi:10.1109/icphm.2019.8819436

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…According to the search results, the MLP proved to be a popular choice. In the works of Giobergia et al ( 2018), Rengasamy et al (2020) and Lee et al (2019), MLP algorithms are implemented for diverse use cases. Giobergia et al ( 2018) make a complete analysis of oxygen sensor data to evaluate the sensor clogging status.…”

Section: Fault Diagnosismentioning

confidence: 99%

“…This approach is tested on a MLP, a Convolutional Neural Network (CNN) and on Recursive Neural Network (RNN) such as Bidirectional Long-Short Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). Lee et al (2019) carry out a performance comparison between ML and deep learning architectures to detect six different faults in automotive safety components. The researchers analyze four deep learning architectures and compare them against three other ML architectures.…”

Section: Fault Diagnosismentioning

confidence: 99%

See 1 more Smart Citation

The asset administration shell as enabler for predictive maintenance: a review

Rahal,

Schwarz,

Sahelices

et al. 2023

J Intell Manuf

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The emergence of the Internet of Things and the interconnection of systems and machines enables the idea of Industry 4.0, a new industrial paradigm with a strong focus on interaction and communication between physical and digital entities, leading to the creation of cyber-physical systems. The digital twin and the standard for the Asset Administration Shell are concepts derived from Industry 4.0 that exploit the advantages of connecting the physical and virtual domains, improving the management and display of the collected data. Furthermore, the increasing availability of data has enabled the implementation of data-driven approaches, such as machine and deep learning models, for predictive maintenance in industrial and automotive applications. This paper provides a two-dimensional review of the Asset Administration Shell and data-driven methods for predictive maintenance, including fault diagnosis and prognostics. Additionally, a digital twin architecture combining the Asset Administration Shell, predictive maintenance and data-driven methods is proposed within the context of the WaVe project.

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Section: Fault Diagnosismentioning

confidence: 99%

Section: Fault Diagnosismentioning

confidence: 99%

The asset administration shell as enabler for predictive maintenance: a review

Rahal,

Schwarz,

Sahelices

et al. 2023

J Intell Manuf

View full text Add to dashboard Cite

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“…Another approach to vibrational analysis utilizes constrained computation and embedded hardware. A Raspberry Pi was used to diagnose six common automotive faults using deep learning as a stable classification method (relative to decision trees), comparing four neural network architectures [35]. It is unclear how these results generalize to other vehicle types and configurations, and whether they are less-sensitive to small data perturbations than other techniques.…”

Section: Vehicle Conditionmentioning

confidence: 99%

“…This research required vehicle to be driven at particular speeds in order to maximize signal. Accuracy varies, with a peak Matthew Correlation Coefficient of 0.994 [53] -however, a small sample size and randomly-generated datasets with replacement may lead to overfit, artificially heightening the reported performance.…”

Section: Wheel Tire and Suspensionmentioning

confidence: 99%

Surveying Off-Board and Extravehicular Monitoring and Progress Towards Pervasive Diagnostics

Siegel¹,

Coda²,

Terwilliger³

2021

SAE Intl. J CAV

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We survey the state-of-the-art in offboard diagnostics for vehicles, their occupants, and environments, with particular focus on vibroacoustic approaches. We identify promising application areas including data-driven management for shared mobility and automated fleets, usage-based insurance, and vehicle, occupant, and environmental state and condition monitoring. We close by exploring the particular application of vibroacoustic monitoring to vehicle diagnostics and prognostics and propose the introduction of automated vehicle-and context-specific model selection as a means of improving algorithm performance, e.g. to enable smartphone-resident diagnostics. The described approach may serve as the first step in developing "universal diagnostics," with applicability extending beyond the automotive domain. Motivating the Need for Vehicle DiagnosticsThe automotive world is changing, and there is increasing concern about vehicles' environmental impact, particularly those with internal combustion engines. As a result, there is a growing prevalence of efficiency-improving systems within vehicles. One of the biggest contributors to lifetime efficiency is the availability of in-vehicle diagnostic systems, as these may report faults early and precisely to motivate owners and operators to seek out preventative or restorative maintenance, enhancing safety and reducing operating costs.At the same time, mobility culture is evolving, transitioning from the need to own a car towards the concept of mobilityas-a-service. Today, vehicle sales are slowing despite continued high mobility demand: The average vehicle age and lifetime miles travelled are increasing, particularly in developing countries [1,2], and shared mobility services, car rentals, and "robotaxis" are emerging. Increased utilization and novel use cases require enhanced fleet data generation and management capabilities. Key to enhanced fleet management is the concept of automotive diagnostics, i.e. the inference of a vehicle's condition based on observed symptoms indicating a technical state [3]. Diagnostics are important for monitoring vehicle, environment, and occupant status (e.g. component wear, road conditions, or driver alertness).Historically, these diagnostics draw upon in-situ sensors and computation to develop "On Board Diagnostics." On-Board Diagnostics (OBD)On-Board Diagnostic (OBD-II) systems present on vehicles sold since 1996 [4] are an automated control system utilizing distributed sensing across a vehicle's embedded systems as a technical solution for measuring vehicle operational parameters and detecting, reporting, and responding to faults.

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