Automotive Predictive Failure System Using Sensor Data Analysis
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Solution Overview
Problem
Current vehicular diagnostic systems fail to efficiently identify and predict component failures, leading to unexpected breakdowns and unsafe circumstances, as they do not provide real-time monitoring and predictive analytics for vehicular parts, relying on trained technicians for diagnosis and relying on user interface indications which are often general or absent.
Innovation Solution
An automotive predictive failure and alerting system that uses vehicle sensors to continuously report performance data to an engine control unit (ECU) and a remote server, which analyzes data to detect deviations and correlate patterns to predict part failures, notifying owners/drivers through email, text, or audio alerts, utilizing primary and secondary datasets to define performance ranges and detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic systems rely on trained technicians performing pass/fail tests, then diagnostic accuracy can be maintained, but diagnostic efficiency and timeliness deteriorate
Solution Approach 1:
The system enables self-diagnosis by equipping vehicles with sensors and onboard computing devices that automatically monitor performance data, detect anomalies, and generate diagnostic assessments without requiring trained technicians to perform manual pass/fail tests
Solution Approach 2:
The system continuously collects performance data from multiple sensors, compares actual values against expected ranges, and provides real-time feedback through user interface indications when deviations are detected, enabling ongoing monitoring rather than periodic technician inspections
2Device complexity
If user interface indicates general warnings without specific problem identification, then system complexity is reduced, but diagnostic information quality deteriorates
Solution Approach 1:
The system segments the diagnostic process into distinct functional modules: sensor data collection, performance range determination, anomaly detection, and user notification. Each module handles specific tasks independently, maintaining system simplicity while preserving comprehensive diagnostic information
Solution Approach 2:
The onboard computing device acts as an intermediary that processes raw sensor data, determines expected performance ranges, detects anomalies, and formulates specific diagnostic assessments before presenting them to the user interface, thereby preserving diagnostic information quality without increasing user-facing complexity
3Reliability
If real-time predictive analytics are implemented, then failure prediction capability is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing performance data from multiple sensors, determining expected performance ranges in advance, and preparing diagnostic assessments before actual failures occur, enabling predictive rather than reactive maintenance
Solution Approach 2:
The onboard computing device performs multiple functions: collecting sensor data, determining performance ranges, detecting anomalies, generating diagnostic assessments, and communicating with user interfaces. This multi-functionality consolidates complexity into a single device rather than requiring separate systems for each function
4Loss of time
If continuous sensor monitoring and remote server analysis are implemented, then diagnostic timeliness is improved, but energy consumption and data transmission requirements worsen
Solution Approach 1:
The system implements periodic action by collecting sensor data at defined intervals, performing anomaly detection at regular checkpoints, and transmitting data to remote servers periodically rather than continuously, thereby maintaining diagnostic timeliness while reducing energy consumption and data transmission requirements
Data Source
AI summary
A method of predicting failure for vehicular components is implemented within a vehicle through a plurality of part sensors and an on-board computing (OBC) device as the part sensors are communicably coupled with a remote server through the OBC device. The OBC device continuously timestamps and uploads a plurality of performance time-dependent data (PTDD) points to the remote server throughout a current vehicular trip. The remote server then analyzes the uploaded PTDD points with an updatable total time duration and an active performance-define range that are calculated from prior vehicular trips. The remote server is then able to identify a potential vehicular problem during the current trip, based upon the uploaded PTDD points. When a potential vehicular problem is detected within the current trip, an annotating assessment is generated and wirelessly sent to a personal computing device of the owner/operator of the vehicle.