Master's Thesis Proposal
Title: Data Analytics for Automotive Diagnosis
Student: Dusan Salovic
Supervisor: Prof. Thilo Sauter
Co-supervisor: Univ. Ass. Ralph Hoch
Matrikelnummer: 12226592
1. Motivation & Problem Statement
We’re all witnessing the rapid developments in the automotive industry. Ensuring the optimal performance and reliability of vehicle components is crucial. Traditional diagnostic approaches often face challenges in indirectly measuring subtle changes in the behavior of critical parts, such as clogged injectors or expanding holes and the subsequent impact on overall system calibration. In response to this, our motivation is to pioneer an innovative data-oriented method for automotive diagnoses that are focused on the indirect measurement of physical reactions induced by intentional adjustments, such as lambda jumps.
The specific type of diagnosis we are addressing involves parts in the vehicle experiencing shifts in behavior that are challenging to measure directly, both in terms of physical changes and operational variations, such as lambda values. Our strategy involves provoking a measurable physical reaction, allowing us to indirectly assess the drift in component calibration. Examples include diagnosing issues related to air-fuel imbalance, fuel purge control, oxygen storage capacity, lambda sensor reactivity and tank leakage.
The conventional diagnostic approaches struggle to answer critical questions related to operating points, data sufficiency, software improvement, calibration completion and overall data understanding. By leveraging advanced data analytics, our goal is to discover dependencies in the physical reactions, acknowledging that parts exist multiple times in a vehicle, influenced by diverse conditions such as engine load, speed, gears and other vehicle characteristics.
2. Research Questions
2.1 List of Quality Criteria for Diagnosis
- Research Question 1: What are the essential quality criteria for automotive diagnosis, encompassing
required signals, alternative signals, signal resolution, variance, coverage of operating points and data
quality dimensions (completeness, timeliness, reliability, uniqueness)?
2.2 Feature Importance for Diagnosis
- Research Question 2: Which signals or combinations of signals and features are crucial for automotive
diagnosis, considering minimum targets (cylinders, banks, gears, operating conditions), maximum targets
(other potential effects) and specific features like torque signal oscillations? How does this analysis
extend to both OK and NOK data?
2.3 Visualization of Diagnostic Results
- Research Question 3: What are effective ways to visualize diagnostic results, incorporating metrics to
determine the adequacy of data/measurements? How can these visualizations aid in decision-making for
diagnosis and provide insights into the quality of the data?
3. Aim of the Work
This thesis aims to design and implement a specialized framework for automotive diagnosis, focusing on
quality criteria, feature importance analysis and innovative visualization methods.
Our approach aims to tackle fundamental questions that often escape the grasp of conventional
approaches:
1. Exploration of Operating Points: Have all necessary operating points been adequately covered in
our diagnostic process?
2. Data and Software Enhancement: Where are the gaps in our data and how can we enhance our
software to improve diagnostic accuracy?
3. Calibration Completion Confirmation: Is our calibration process complete and do the
components align precisely with the desired specifications?
4. Comprehensive Data Understanding: Can we achieve a holistic understanding of the data with a
single glance, facilitating quicker and more informed decision-making?
5. Dependency Recognition: Do we have a comprehensive understanding of dependencies in the
data and can we consider them for diagnosis or are there uncontrollable factors necessitating
specific interventions?
Our goal is to revolutionize automotive diagnoses through a data-centric approach, addressing inherent
challenges in traditional methods and providing a more detailed and comprehensive understanding of
vehicle component behaviors.
4. Methodological Approach
In alignment with the work structure and the challenge of data-oriented solutions for automotive
diagnosis, the CRISP-DM framework will be performed as follows:
4.1 Problem Understanding
Step 1: Define Objectives for Automotive Diagnosis
- Clearly define the objectives of developing a data-oriented solution for automotive diagnosis,
emphasizing quality criteria, feature importance and effective visualization.
Step 2: Assess Challenges in Current Diagnostic Practices
- Identify and understand the current challenges in automotive diagnostics, particularly focusing on
manual processes and the lack of a standardized approach.
4.2 Data Understanding
Step 3: Data Collection for Diagnosis
- Gather relevant time-series data for automotive diagnosis, considering signals from diverse sources
such as sensors and measurement files.
Step 4: Explore Diagnostic Data
- Conduct exploratory data analysis to understand the characteristics of automotive diagnostic data and
identify potential challenges.
4.3 Data Preparation for Diagnosis
Step 5: Define Quality Criteria for Diagnosis
- Compile a refined set of quality criteria tailored to automotive diagnosis, covering signals, resolution,
variance and data quality dimensions.
Step 6: Analyze Feature Importance for Diagnosis
- Utilize advanced analytics to determine the importance of signals and features specifically in the
context of automotive diagnosis, considering OK and NOK data.
4.4 Modeling for Diagnosis
Step 7: Algorithm Selection for Feature Importance Analysis
- Choose suitable algorithms for feature importance analysis, ensuring they align with the diagnostic
objectives.
Step 8: Construct Models for Diagnostic Insights
- Develop models to extract the main dependencies in diagnostic data, pinpoint root causes for errors
and identify critical features for effective diagnosis.
4.5 Evaluation in the Context of Diagnosis
Step 9: Develop Visualization Techniques for Diagnostic Results
- Innovate visualization methods tailored to present diagnostic results effectively, incorporating metrics
to assess data adequacy.
Step 10: Evaluate Diagnostic Model Effectiveness
- Assess the effectiveness of the framework in enhancing automotive diagnostic processes, emphasizing
improvements in accuracy, efficiency and cognitive load.
4.6 Deployment for Automotive Diagnosis
Step 11: Implement the Data-Oriented Framework for Diagnosis
- Implement the developed framework, ensuring seamless integration into automotive diagnostic
workflows.
Step 12: Deploy Framework for Real-world Diagnostic Scenarios
- Deploy the framework in real-world automotive diagnostic scenarios, assessing its practicality and
impact on decision-making.
4.7 Documentation and Communication
Step 13: Document Findings and Recommendations
- Thoroughly document and communicate the findings, insights and recommendations derived from the
developed data-oriented solution for automotive diagnosis.


CAN YOU WRITE ME ALL SECTIONS AND SUBSECTIONS FOR MY MASTER THESIS?