Predictive Maintenance – Analysis of Seasonal Dependence of Vehicle Engine Faults

This paper presents methods and results for the analysis of interrelationships between the occurrence of specific engine faults according to seasons. The issue of maintenance is substantial for the automotive industry and improvements are requested due to the enhancement of profitability. Findings of this paper are based on logged-vehicle data from 760.976 vehicles provided by the company Geotab. Utilization of such data gains importance for the automotive service sector with special regard of increasing importance of predictive maintenance. The visualization of the data of three different engine faults was realized with the free graphic and statistics program “Tableau Desktop 2018.1” as well as “IBM SPSS Statistics Subscription Trial for Mac OS”. The result is that the tested interrelations are significant, leading to the conclusion that the engine faults of “vehicle battery has low voltage”, “low priority warning light on” and “general warning light on” are dependent of seasons. This finding can be used to help car manufacturers and car service providers to reduce maintenance costs. Keywords: automotive, big data, predictive maintenance, seasonal engine faults, vehicle error code

Capacity and Impedance Estimation by Analysing and Modeling in Real Time Incremental Capacity Curves

The estimation of lithium ion capacity fade and impedance rise on real application is always a challenging work due to the associated complexity. This work envisages the study of the battery charging profile indicators (CPI) to estimate battery health indicators (capacity and resistance, BHI), for high energy density lithium-ion batteries. Di erent incremental capacity (IC) parameters of the charging profile will be studied and compared to the battery capacity and resistance, in order to identify the data with the best correlation. In this sense, the constant voltage (CV) step duration, the magnitudes of the IC curve peaks, and the position of these peaks will be studied. Additionally, the behaviour of the IC curve will be modeled to determine if there is any correlation between the IC model parameters and the capacity and resistance. Results show that the developed IC parameter calculation and the correlation strategy are able to evaluate the SOH with less than 1% mean error for capacity and resistance estimation. The algorithm has been implemented on a real battery module and validated on a real platform, emulating heavy duty application conditions. In this preliminary validation, 1% and 3% error has been quantified for capacity and resistance estimation.Funding: This work and the project hifi-elements has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 769935

Prognostics Health Estimation of Lithium-ion Batteries in Charge-Decay Estimation Uncertainties – A Comparative Analysis

This study uses nonlinear mixed effect-based degradation modeling that considers the influence of uncertainties on the state-of-charge of lithium-ion batteries to determine the State-of-Health (SOH) of the batteries at different End-of-Life (EOL) failure thresholds. The results of the analysis obtained with lithium-ion batteries data from NASA Ames Centre repository, confirms that the SOH of the batteries is influenced by the uncertainties. This is because the random effects models show a better correlation with the experimental data than the fixed effects models that have not considered uncertainty. It is important therefore that battery prognosis is done in consideration of these parametric uncertainties, to forestall poor estimation of the SOH of the lithium-ion batteries at various stages of the lifecycle. Seeing that the presence of uncertainties could result in unwarranted failures of assets powered by the batteries, due to over-estimation of the remaining useful life (RUL) or capital loss, due to early decommissioning of efficient batteries when the RUL is under-estimated

A novel remaining useful life prediction framework for lithium-ion battery using grey model and particle filtering

An accurate remaining useful life (RUL) prediction method is significant to optimize the lithium-ion batteries' performances in an intelligent battery management system. Since the construction of battery models and the initialization of algorithms require a large amount of data, it is difficult for conventional methods to guarantee the RUL prediction accuracy when the available data are insufficient. To solve this problem, a synergy of sliding-window grey model (SGM) and particle filter (PF) is exploited to build an innovative framework for battery RUL prediction. The SGM is adopted to explore the modelling of battery capacity degradation, and it characterizes the capacity changes during the battery's life-time with a few data (eg, 8 data points). To promote the accuracy and traceability of prediction, the development coefficient of the SGM, which can dynamically reflect the capacity degradation, is extracted to update the state variables of state transition function in PF. Accordingly, the fusion of SGM and PF (SGM-PF) can extrapolate the changes of the capacity and realize RUL prediction using fewer data. Furthermore, the performances of SGM-PF are comprehensively validated using two types of batteries aged under different conditions. The RUL prediction results reveal that the SGM-PF framework can achieve precise and reliable predictions in different prediction horizons with as few as 8 data points, and it has prominent performance in accuracy and stability over contrastive methods, especially in long-term prognosis

A Review on Fault Mechanism and Diagnosis Approach for Li-Ion Batteries

Li-ion battery has attracted more and more attention as it is a promising storage device which has long service life, higher energy, and power density. However, battery ageing always occurs during operation and leads to performance degradation and system fault which not only causes inconvenience, but also risks serious consequences such as thermal runaway or even explosion. This paper reviews recent research and development of ageing mechanisms of Li-ion batteries to understand the origins and symptoms of Li-ion battery faults. Common ageing factors are covered with their effects and consequences. Through ageing tests, relationship between performance and ageing factors, as well as cross-dependence among factors can be quantified. Summary of recent research about fault diagnosis technology for Li-ion batteries is concluded with their cons and pros. The suggestions on novel fault diagnosis approach and remaining challenges are provided at the end of this paper

Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies

Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter

Reaction wheel (RW), the most common Attitude Control Systems (ACS) in satellites, are highly prone to failure. A satellite needs to be oriented in a particular direction to maneuver and accomplish its mission goals; losing the RW can lead to a complete or partial mission failure. Therefore, estimating the remaining useful life (RUL) in long and short spans can be extremely valuable. The short-period prediction allows the satellite\u27s operator to manage and prioritize mission tasks based on the RUL and increases the chances of a total mission failure becoming a partial one. Studies show that lack of proper bearing lubrication and uneven frictional torque distribution, which lead to variation in motor torque, are the leading causes of failure in RWs. Hence, this study aims to develop a three-step prognostic method for longterm RUL estimation of RWs based on the remaining lubricant for the bearing unit and potential fault in the supplementary lubrication system. In the first step of this method, the temperature of the lubricants is estimated as the non-measurable state of the system, using a proposed Adaptive particle filter (APF) with an-gular velocity and motor current of RW as the available measurements. In the second step, the estimated lubricant\u27s temperature and amount of injected lubrication in the bearing alongside the lubrication degradation model are fed to a two-step Particle Filter (PF) for online model parameter estimation. In the last step, the performance of the proposed prognostics method is evaluated by predicting the RW\u27s RUL under two fault scenarios, including excessive loss of lubrication and insufficient injection of lubrication. The results show promising performance for the proposed scheme with accuracy in estimation of degradation model\u27s parameters around 2–3% of root mean squared percentage error (RMSPE) and prediction of RUL around 0.1- 4% percentage error