Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154104/1/kampfner1996.pd

Intelligent fault detection and classification based on hybrid deep learning methods for Hardware-in-the-Loop test of automotive software systems

Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

Identifying Onboard Diagnosis-Relevant Errors of an SCR System, using Neural Networks

The selective catalytic reduction system (SCR) is responsible for the reduction of harmful NO x gases in vehicles. A faulty SCR system causes higher emissions of NO x gases, which results in multiple environmental and health hazards. This important role played by the SCR system is the reason for selecting this topic for my thesis. The goal of this thesis is to evaluate whether a neural network can be implemented for the easy analysis and classification of the various possible errors that could tamper with the proper functioning of the SCR system. The data required for the learning algorithm is already available with IAV. In this thesis report, I shall discuss an approach for the identification of SCR errors using a neural network on the already available data. The results of this thesis could have a positive impact on the environment as well as on the health of the people. The results of this thesis would be of key importance for the automotive field and for industries that use diesel

Development and Demonstration of Control Strategies for a Common Rail Direct Injection Armoured Fighting Vehicle Engine

The development of a controller which can be used for engines used in armoured fighting vehicles is discussed. This involved choosing a state of the art reference common rail automotive Diesel engine and setting-up of a transient engine testing facility. The dynamometer through special real-time software was controlled to vary the engine speed and throttle position. The reference engine was first tested with its stock ECU and its bounds of operation were identified. Several software modules were developed in-house in stages and evaluated on special test benches before being integrated and tested on the reference engine. Complete engine control software was thus developed in Simulink and flashed on to an open engine controller which was then interfaced with the engine. The developed control software includes strategies for closed loop control of fuel rail pressure, boost pressure, idle speed, coolant temperature based engine de-rating, control of fuel injection timing, duration and number of injections per cycle based on engine speed and driver input. The developed control algorithms also facilitated online calibration of engine maps and manual over-ride and control of engine parameters whenever required. The software was further tuned under transient conditions on the actual engine for close control of various parameters including rail pressure, idling speed and boost pressure. Finally, the developed control strategies were successfully demonstrated and validated on the reference engine being loaded on customised transient cycles on the transient engine testing facility with inputs based on military driving conditions. The developed controller can be scaled up for armoured fighting vehicle engines

Classification of crankshaft remanufacturing using Mahalanobis-Taguchi system

Remanufacturing is a process of returning a used product to at least its original performance with a warranty that is equivalent or better than that of a newly manufactured product. During a preliminary inspection on remanufacturing companies, it was found that there is no end life for crankshafts in terms of classifying it either to remanufacture, repair or reject due to limited information provided by the original equipment manufacturer. The manufacturer did not provide any information on the annual quantity produced and their specifications to the remanufacturing company for the purpose of referencing. Eventually, the distinctiveness of the remanufactured crankshaft from the original cannot be measured. Thus, the aim of this work is to classify crankshafts' end life into recovery operations based on the Mahalanobis-Taguchi System. The crankpin diameter of six engine models were measured in order to develop a scale that represents their population in a scatter diagram. It was found that on the diagram of each engine model, the left distributions from the center point belong to rejected crankshafts, the right distributions belong to re-manufacturable crankshafts, and the upper distributions belong to the repairable crankshafts. The developed scale is believed to be able to help remanufacturers instantaneously identify and match any unknown model crankshafts to its right category. The Ministry of International Trade & Industry (MITI) has established a remanufacturing policy under RMK11 and put in efforts to encourage Malaysians to venture into the remanufacturing business. Thus, this model will help the industry to understand and formulate their decision-making to sustain the end of life of their products

Automotive Powertrain Control — A Survey

This paper surveys recent and historical publications on automotive powertrain control. Control-oriented models of gasoline and diesel engines and their aftertreatment systems are reviewed, and challenging control problems for conventional engines, hybrid vehicles and fuel cell powertrains are discussed. Fundamentals are revisited and advancements are highlighted. A comprehensive list of references is provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72023/1/j.1934-6093.2006.tb00275.x.pd

Powertrain Architectures and Technologies for New Emission and Fuel Consumption Standards

New powertrain design is highly influenced by CO2 and pollutant limits defined by legislations, the demand of fuel economy in for real conditions, high performances and acceptable cost. To reach the requirements coming from both end-users and legislations, several powertrain architectures and engine technologies are possible (e.g. SI or CI engines), with many new technologies, new fuels, and different degree of electrification. The benefits and costs given by the possible architectures and technology mix must be accurately evaluated by means of objective procedures and tools in order to choose among the best alternatives. This work presents a basic design methodology and a comparison at concept level of the main powertrain architectures and technologies that are currently being developed, considering technical benefits and their cost effectiveness. The analysis is carried out on the basis of studies from the technical literature, integrating missing data with evaluations performed by means of powertrain-vehicle simplified models, considering the most important powertrain architectures. Technology pathways for passenger cars up to 2025 and beyond have been defined. After that, with support of more detailed models and experimentations, the investigation has been focused on the more promising technologies to improve internal combustion engine, such as: water injection, low temperature combustions and heat recovery systems

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154106/1/kampfner1998.pd