A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

Tang, Shuang; Gu, Jinan; Tang, Keming; Zou, Rong; Sun, Xingming; Uddin, Saad

doi:10.3390/app9061080

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…In particular, the hydraulic valve is the core control component of the hydraulic system, and it is widely used in numerous engineering applications to control the flow and pressure of fluids [4][5][6]. In the hydraulic system, a vibration analysis (VA) is the most popular and efficient condition monitoring technique for rotating systems including the hydraulic pump, electric motor, bearing, and more [7][8][9][10][11][12][13][14][15][16]. However, the working process of the valve core of the hydraulic valve is a reciprocating motion.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

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show abstract

“…In terms of VA, wireless microphones have been used to monitor wheel bearings and identify defects based on frequency-domain features [49], and vibration analysis has been implemented o detect remaining useful life of mechanical components such as bearings [50]. Similar data sources and algorithms have been exploited to identify the emergence of cracks in suspension beams [51].…”

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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Effects of ageing on low-cycle fatigue (LCF) properties of AZ80 magnesium alloy wheel hub

Wang

Yang

et al. 2020

Mater. Res. Express

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A hollow billet Expanding-Reducing Extrusion (ERE) wheel hub forming process was carried out with AZ80 magnesium alloy. The effects of ageing on Low-cycle fatigue (LCF) properties were investigated, and the microstructure was characterized with scanning electronic microscopy (SEM). The LCF testing results exhibited that the stress amplitude increased with increasing ageing time, while the plastic strain decreased with increasing ageing time. Cyclic softening followed by hardening was observed for 0 h and 16 h aged samples; however, there was no obvious cyclic softening or hardening for 36 h aged sample. The tension-compression asymmetry were not obvious due to the low strain, precipitation and the non typical basal texture. Fracture observations indicated that the fatigue cracks were initiated on the surface, and the crack propagation area decreased with increasing ageing time. Thus, it can be concluded that ageing has strong effects on the fatigue life for the studied magnesium alloy.

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A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings

Cited by 6 publications

References 37 publications

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

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

Effects of ageing on low-cycle fatigue (LCF) properties of AZ80 magnesium alloy wheel hub

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