Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data

Prytz, Rune; Nowaczyk, Sławomir; Rögnvaldsson, Thorsteinn; Byttner, Stefan

doi:10.1016/j.engappai.2015.02.009

Cited by 133 publications

(61 citation statements)

References 27 publications

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“…A similar approach is described in the recent publication [14], where random forest models are derived from logged on-board data for estimating the likelihood that air compressors in commercial trucks and buses survive until the next service stops.…”

Section: Related Workmentioning

confidence: 97%

Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data

Bergmeir

Nitsche

Nonnast

et al. 2015

Neural Comput & Applic

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Component failures in hybrid electric vehicles (HEV) can cause high warranty costs for car manufacturers. Hence, in order to (1) predict whether a component of the hybrid power-train of a HEV is faulty, and (2) to identify loads related to component failures, we train several random forest variants on so-called load spectrum data, i.e., the state-of-the-art data employed for calculating the fatigue life of components in fatigue analysis. We propose a parameter tuning framework that enables the studied random forest models, formed by univariate and multivariate decision trees, respectively, to handle the class imbalance problem of our dataset and to select only a small number of relevant variables in order to improve classification performance and to identify failure-related variables. By achieving an average balanced accuracy value of 85.2 %, while reducing the number of variables used from 590 to 22 variables, our results for failures of the hybrid car battery (approx. 200 faulty, 7000 non-faulty vehicles) demonstrate that especially balanced random forests using univariate decision trees achieve promising classification results on load spectrum data. Moreover, the selected variables can be related to component failures of the hybrid power-train.Keywords Hybrid electric vehicle Á Balanced random forests Á Univariate and multivariate decision trees Á Orthogonal and oblique splits Á Load spectrum data

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Section: Related Workmentioning

confidence: 97%

Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data

Bergmeir

Nitsche

Nonnast

et al. 2015

Neural Comput & Applic

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“…There is a rich history of research on vehicle predictive maintenance focusing on single vehicle readouts [1,2]. However, relying on single readouts to predict possible future failures means conditioning the future on current observed state of the vehicle only.…”

Section: Introductionmentioning

confidence: 99%

Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life

Mashhadi¹,

Nowaczyk²,

Pashami³

2019

Applied Sciences

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Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers.

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“…Currently the mechanics do not have reliable ways to measure condition of the air compressor, so a system like the one we propose here could lead to significant cost savings. This component is problematic enough to warrant a dedicated, supervised solution (Prytz et al, 2015).…”

Section: Autoencodersmentioning

confidence: 99%

Self-monitoring for maintenance of vehicle fleets

Rögnvaldsson

Nowaczyk

Byttner

et al. 2017

Data Min Knowl Disc

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An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the 123Self-monitoring for maintenance of vehicle fleets 345 cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g., a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost 4 years.

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Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data

Cited by 133 publications

References 27 publications

Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data

Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data

Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life

Self-monitoring for maintenance of vehicle fleets

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