Collecting and processing dependability-related information in car building companies
Bibliographic record
Abstract
Passenger cars are complex technical products. They consist of units, assemblies, and components that are characterized by a certain combination of interacting parts. Additionally, modern passenger cars feature significant numbers of automatic subsystems and automated components: air conditioning, electric heating systems, lighting systems, compartment doors, exterior doors, etc. The process of collecting data on the technical condition of products is to ensure the regularity, reliability, timeliness, and completeness of information. It is known that products most clearly manifest their quality and dependability in operation. A competent organisation of the collection and processing of information on a product’s dependability allows obtaining reliable information on its health and performance. In the course of operation, the connections between individual units and components of passenger cars may become disrupted, the fasteners of individual parts and sensors may become loose, rubber seals may become naturally worn. All of that causes performance decline, as well as malfunctions and failures. Preventing a sharp increase in the number of failures requires performing a number of preventive actions aimed at identifying and eliminating faults, as well as preventing their root causes. First and foremost, such measures include rolling stock maintenance and overhaul. All such activities are strictly regulated in the operating manuals of both a car and its components. The specified life of passenger cars, as well as their components and units, varies roughly from 20 to 40 years. Some components of passenger cars have been in production with no major modifications since the early 2000s. That suggests that a product’s dependability can be evaluated comprehensively throughout the entire life cycle. But that can only be done by collecting and processing a significant amount of information on malfunctions obtained both during the warranty and post-warranty periods. The information is to come from various sources, i.e., operating companies, service depots, car repair plants that carry out overhauls. This most valuable information is to be accumulated and be digitalisable. This paper addresses a number of matters associated with the collection, validation, and recording of faults and failures of passenger car components. Aim. To examine the state-of-the-art systems that collect and process fault data in engineering companies and to suggest algorithmic and methodological solutions to improve the degree of automation of failure information processing. Methods. The paper uses methods of system analysis and software engineering. Conclusions. An algorithm for recording product failures according to incoming documents is proposed. Software solutions have been developed to automate the process of collecting and processing data on malfunctions of passenger car components. The authors examined a method of tracking the warranty fleet required for defining the total operating time as part of calculating the dependability indicators of passenger car components in operation. A failure code list was proposed that takes into account the specificity of the structural relationships between passenger car components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".