Prognostics Model to Predict Brake Rotor Thickness Variation

Kazemi, Hamed; Du, Xinyu; Drame, Samba; Dixon, Regan; Sadjadi, H. Mohseni

doi:10.36001/phmconf.2019.v11i1.796

Cited by 3 publications

(5 citation statements)

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“…As described in our previous work (Kazemi, et al 2019), three vehicle signals are selected to extract fault signatures based on vibration characteristics. This research work is preceded by a set of well-defined requirements the brake rotor fault detection algorithm must meet.…”

Section: Thicknessmentioning

confidence: 99%

“…In total, 25 faulty rotors were created. Details of the fault injection procedure can be found in (Kazemi et al, 2019). To create faulty rotors, healthy rotors were machined down to generate varying levels of thickness variation (see Figure 2).…”

Section: Fault Injectionmentioning

confidence: 99%

“…Both identification and isolation of RTV faults are accomplished by performing time and frequency analysis on the brake BCP, AX, and wheel speed signals. More details about the correlation between these signals and rotor fault severity can be found in our previous works (Kazemi, Du, Dixon, & Sadjadi, 2019). Once a fault is detected, this system triggers notifications to make the customer aware of any needed service.…”

Section: Brake Rotor Health Monitoring Algorithmmentioning

confidence: 99%

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Method to Detect and Isolate Brake Rotor Thickness Variation and Corrosion

Kazemi

Du²,

Sadjadi³

2023

IJPHM

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Brake rotors are essential parts of the disc brake systems. Brake rotor thickness variation (RTV) and corrosion are among top failure modes for brake rotors, which may lead to brake judder and pulsation, steering wheel oscillations and chassis vibration. To improve customer satisfaction, vehicle serviceability and availability, it is necessary to develop an onboard fault detection and isolation solution. This study presents a methodology to monitor the state-of-health of brake rotor system to reduce costs associated with scheduled inspection for autonomous fleet or corrective maintenance. We converted the vehicle signals from time-domain to angle-domain and determined health indicators to estimate the RTV level of the rotors. Variance, envelope and order analysis of the brake circuit pressure, longitudinal acceleration and wheel speed sensor signals in angle-domain were promising health indicators to differentiate healthy and faulty rotors. A classification model was developed to fuse the health indicators and estimate the state-of-health of the rotors to report the most degraded rotor with corner isolation. Results showed that using this concept we were able to detect failure levels of 20 microns and larger and meet the customer requirement. Robustness analysis showed that the concept is robust to the noise factors of tire type, tire pressure and vehicle weight. The sensitivity analysis showed that the algorithm is sensitive to two of the calibration parameters (i.e., brake pedal position gradient (BPPG) threshold and the filter order used to derive BPPG) used to determine the brake event and enable the algorithm.

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Section: Thicknessmentioning

confidence: 99%

Section: Fault Injectionmentioning

confidence: 99%

Section: Brake Rotor Health Monitoring Algorithmmentioning

confidence: 99%

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Method to Detect and Isolate Brake Rotor Thickness Variation and Corrosion

Kazemi

Du²,

Sadjadi³

2023

IJPHM

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“…In our previous work (Kazemi, Du, Drame, Dixon, and Sadjadi (2019), Du, Mai, Kazemi, and Sadjadi (2020) Jalaliyazdi, Garner, Sadjadi, and Kazemi ( 2021)), we developed a methodology to monitor health of brake rotors and generated health indicators to differentiate between a healthy brake rotor and a degraded rotor due to thickness variation. In this paper, we demonstrate the application of the rank HI framework to that study to demonstrate the effectiveness of such framework in selecting the best performing features.…”

Section: Case Study -Application In Brake Rotor Prognosticsmentioning

confidence: 99%

“…detrend, derivative, phase domain transformation), and postprocessing (e.g. variance, envelope, skewness, kurtosis, amplitude of average order spectrum) methods (Kazemi et al 2019) were applied to MCP, WS Sensor and AX in both time and frequency domain which resulted in generating hundreds of health indicators. Regression analysis were then performed on these features to model the wheel RTV.…”

Section: Health Indicatorsmentioning

confidence: 99%

A Framework to Rank Prognostics Health Indicators with Application to Brake Rotors

Kazemi

Du²,

Sadjadi

2022

PHM_CONF

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This study presents a framework to assess the effectiveness of various health indicators (HIs) used to monitor the state of health (SOH) of a brake rotor health monitoring system. The following criteria were used to rank various health indicators: (i) Identifiability: Correlation of the HI with the Ground Truth (GT); (ii) Compactness: Mean of the standard deviation of the estimated SOHs; (iii) Robustness to Noise Factors: An HI is considered robust when it meets all functional and customer requirements under all operating conditions and its performance is not affected by the variations in the environment, operating conditions or other factors impacting the performance in an undesired way (noise factors); (iv) Monotonicity: To quantify the monotonic trend in HIs as the fault level increases from healthy baseline to the most severe faults. Monotone HIs are preferred as they will likely generalize better to data not used in development; and (v) Estimation Error: The average relative error between the GT and the prediction obtained from the regression analysis. Results showed that this framework can be applied to several HIs derived from performing time and frequency analysis on various sensor signals used to monitor the health of brake rotors. Top HIs selected based on this framework provided the best performance in detecting degraded brake rotors as evidenced by higher classification score.

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Fault Detection and Isolation for Brake Rotor Thickness Variation

Mai²,

Kazemi³

et al. 2020

PHM_CONF

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Brake rotors are critical parts of the disc braking system for modern vehicles. One common failure for brake rotors is the thickness variation, which may result in unpleasant brake pulsation, vehicle vibration during braking, or eventually lead to the malfunction of the braking system. In order to improve customer satisfaction, vehicle serviceability and availability, it is necessary to develop an onboard fault detection and isolation solution. In our previous work, the vibration features of master cylinder pressure, vehicle longitudinal acceleration and wheel speed were identified as fault signatures. Based on these fault signatures, a vibration- based fault detection and isolation algorithm is developed in this work. The difference of frequency response between the braking period and the normal driving period (non-braking) is employed to improve the algorithm robustness. The experiment results demonstrate the proposed algorithm can robustly diagnose the thickness variation fault and isolate the fault to each vehicle corner.

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Prognostics Model to Predict Brake Rotor Thickness Variation

Cited by 3 publications

References 0 publications

Method to Detect and Isolate Brake Rotor Thickness Variation and Corrosion

Method to Detect and Isolate Brake Rotor Thickness Variation and Corrosion

A Framework to Rank Prognostics Health Indicators with Application to Brake Rotors

Fault Detection and Isolation for Brake Rotor Thickness Variation

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