Although bearing condition monitoring and fault diagnosis is a widely studied and mature field, applications to automotive wheel bearings have received little attention. This is likely due to the lack of business case, as the vehicle’s four wheel bearings are typically designed to last the vehicle life with low failure rates. Rapid advancements in battery technology are expected to open the door for EVs with million-mile lifespans, exceeding the reliable life of existing low-cost wheel bearing designs. Vehicle designers and fleet owners must choose between paying a higher price for bearings with a longer life or replacing wheel bearings periodically throughout the vehicle life. The latter strategy can be implemented most effectively with the implementation of a low-cost fault detection system on the vehicle. To develop such a system, data from systems with healthy and faulty wheel bearings is needed. This paper discusses the options for generating this data, such as simulation, bench tests, and vehicle-level tests. The challenges and limitations of each are explored, and the specific challenges of developing an approach for a wheel bearing fault detection system are discussed in detail. A method for injecting Brinell Dent failures is developed, and the results of injecting a total of 40 faulty wheel bearings are presented. Metrics of measuring and summarizing the ground-truth health of a wheel bearing using vibration signals recorded on a test bench are explored. These wheel bearings are used to collect preliminary vehicle data, and some initial analysis is shared highlighting the differences between healthy and faulty wheel bearings, setting the stage for future work to develop a low-cost wheel bearing fault detection system.
The automotive industry is undergoing a period of rapid advancement, as OEMs race to develop the next generation of electric and autonomous vehicles. Many manufacturers are investing in prognostics technology, which has made advancements mainly in the aerospace industry over the past couple decades. Unlike aerospace applications, which have relatively more safety-critical systems, it can be more challenging to identify a business case for developing a prognostics or early fault detection system for an automotive application. In the retail setting, early fault detection systems may increase warranty costs, and the benefits to customer satisfaction may not be worth this additional cost. For fleet managers who own and operate many vehicles, however, a business case can be made based on the value of preventing unexpected downtime and unnecessary maintenance. Developing a reliable early fault detection algorithm for a complex system can be an expensive undertaking, requiring many parts, months of data collection, and possibly years of effort, so it is important to understand the possible return on investment for the effort. In this paper, we present a method to model the business value of an early fault detection system. The method is generic and may be applied to any system where the failure modes are purely fatigue based (i.e. abuse modes are excluded), and the failure rate of each part in the system can be independently modelled using a time-to-failure probability density function. The model is based on Monte Carlo simulation, and the assumptions and limitations are explored. The model can be used to estimate the expected savings from implementing an early fault detection system and derive requirements on the true positive and false positive rates required for the fault detection system to meet its business objectives. An example is presented with application to a two-stage gearbox, such as one that may be found in an electric vehicle powertrain. The example shows how to estimate the parameters for each component, how to estimate the costs associated with failure, and ultimately how to interpret the model outputs and drive business decisions.
The Electric Power Steering (EPS) System provides steering assist in conventional vehicle driving and is the main actuator for vehicle lateral control in active safety features. While the driver can sometimes compensate for reduced or loss of steering assist caused by EPS mechanical or electrical degradations, it may become very difficult to steer for larger vehicles. Furthermore, active safety functions cannot control the vehicle effectively for lateral motions without a healthy EPS system. Hence, comprehensive EPS system fault monitoring is essential for the next generation of vehicles. Previous works have utilized computer simulation and hardware-in-the-loop experiments to develop fault diagnosis and prognosis algorithms for electrical and mechanical failures in EPS systems. Using test drive data collected, this paper validates and refines a previously developed algorithm designed for detecting increases in EPS system internal mechanical friction. The data include 215 minutes of natural driving with different speeds and steering maneuvers. Noise factors such as tire type and level of friction introduced are also considered. The previous algorithm is refined to enhance performance addressing issues of time delays and parameter uncertainty specific to the previous model-based algorithm. Specifically, a Kalman filter-based joint state-parameter estimator that uses a simplified vehicle dynamic model is developed to provide a direct estimate of steering friction increase. Data collected from test drives indicate that the refined algorithm can robustly indicate a friction increase before an average human driver notices a difference in steering feel.
With recent developments of energy efficient design and control for electric motors, electrical subsystems and components have become integral parts of main actuators in vehicle systems (e.g., steering and propulsion systems). To ensure proper vehicle operations, it is important to make sure that electrical power is properly transmitted through the power circuit from vehicle power source to the electric motor. However, degradation in the power circuit health, which often manifests itself as increased resistance, may affect power transmission and degrade the system performance. For example, in Electric Power Steering (EPS) systems, if the EPS power circuit resistance is increased and the EPS is drawing power to assist the driver, voltage at the EPS module will drop significantly, causing the EPS to reset and, consequently, Loss of Assist (LOA) incidents. Due to compliance in the steering system and suspension design, drivers often feel that the steering system is fighting back when an LOA incident occurs. While previous work has partially addressed this issue by developing algorithms that estimate resistance increase in EPS power circuits, this paper further validates and refines the algorithms for vehicle on-board and off-board implementations using test drive data collected. Since on-board and off-board implementations impose different limits on signal sampling rates, a total of 250 and 465 minutes of data are respectively collected with various vehicle speeds and steering maneuvers. Moreover, a supervisory control solution, referred to as EPS Anti-Loss-of-Assist (ALOA), is proposed that gradually and proactively reduces EPS torque assist as resistance in the EPS power circuit increases so that the EPS voltage is kept above a resetting threshold. Stationary steering tests of the proposed solution as well as demonstrations on parking lot maneuvers at General Motors Milford Proving Grounds are conducted. The stationary steering tests and demonstrations show that, with the proposed supervisory control, negative effects of increased EPS power circuit resistance can be mitigated without noticeable changes in normal driving experience.
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