2018-01-1357 Published 0 3 Apr 2018 © 2018 SAE International; Ford Motor Company.Structural Analysis Based Sensor Placement for Diagnosis of Clutch Faults in Automatic Transmissions Eeshan Vijay Deosthale, Qadeer Ahmed, Mukilan Arasu, and Giorgio Rizzoni The Ohio State University Majed Mohammed and Richard Hathaway Ford Motor Company Citation: Deosthale, E.V., Ahmed, Q., Arasu, M., Rizzoni, G. et al., “Structural Analysis Based Sensor Placement for Diagnosis of Clutch Faults in Automatic Transmissions,” SAE Technical Paper 2018-01-1357, 2018, doi:10.4271/2018-01-1357. Abstract This paper describes a systematic approach to identify the best sensor combination by performing sensor placement analysis to detect and isolate clutch stuck-off faults in Automatic Transmissions (AT) based on structural analysis. When an engaged clutch in the AT loses pressure during operation, it is classified as a clutch stuck-off fault. AT can enter in neutral state because of these faults; causing loss of power at wheels. Identifying the sensors to detect and isolate these faults is important in the early stage of the AT develop - ment. A universal approach to develop a structural model of an AT is presented based on the kinematic relationships of the planetary gear set elements. Sensor placement analysis is then performed to determine the sensor locations to detect and isolate the clutch stuck-off faults using speed sensors and clutch pressure sensors. The proposed approach is then applied to a 10-Speed AT to demonstrate its effectiveness. A simulator is developed to qualitatively study the effects of clutch stuck-off faults on speeds of different elements in an AT. Simulator results are presented to support the sensor placement analysis. Later, a comparative analysis of different sensor sets based on the cost and performance is conducted to choose the optimal sensor combination. This paper concludes by discussing in detail the different sensor sets that give different fault isolation performance and suggests that only increasing number of sensors does not guarantee better fault isolation. Introduction Automatic Transmissions (AT) are the most widely used transmissions in the automotive industry in The United States [ 1]. The AT system consists of a Torque Converter (TC), Planetary Gear Train, clutches and band brakes operated by hydraulic pressure, a hydraulic pump and a Transmission Control Unit (TCU). The clutches and brakes are selectively engaged by applying hydraulic pressure gener - ated by the hydraulic pump to achieve various speed ratios. The command to engage and disengage the clutches and brakes is issued by the TCU based on multiple variables such as vehicle speed, driver torque request, engine operating conditions etc. The current trend in ATs is to have an increased number of speed ratios to improve drivability, fuel economy and customer satisfaction. This, at the same time, puts an addi - tional emphasis on ensuring that the electronic monitoring and controls system has a high level of integrity in terms of fault diagnostics and can detect and isolate all possible critical faults in the system. Since the number of gears are increasing, it is critical to detect and isolate the faults in each of the gears; as undetected or improper fault response may adversely affect the drivability of the vehicle and result in customer dissatisfaction.Various efforts have been taken towards the fault diag - nosis of automotive transmissions. Xueyuan et al [ 2] used a fuzzy logic and neural network based approach to carry out fault self-diagnosis of AT. Olsson et al [ 3] presented a generic approach to fault diagnostics that combines signal processing, statistics, machine learning and case-based reasoning for on-board and off-board diagnosis. This approach is applied to detect clutch slippage in AT in heavy duty machines. Qu et al [ 4] presented a production rule based fault diagnosis expert system structure design for electronic control AT