Automotive Transmission Anomaly Detection for Predictive Maintenance
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Solution Overview
Problem
Automotive transmission systems face reduced lifespan due to varying driving styles and lack of effective predictive maintenance, leading to potential hazards and increased repair costs.
Innovation Solution
An artificial neural network (ANN) is configured to monitor and analyze sensor data from automotive transmissions, predicting maintenance needs and alerting drivers or autonomous systems to potential issues, with data storage and machine learning algorithms for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for transmission systems, then device complexity is low, but reliability is reduced due to inability to predict maintenance needs
Solution Approach 1:
The patent replaces traditional mechanical monitoring approaches with an artificial neural network-based predictive maintenance system. Sensors collect transmission data (temperature, pressure, vibrations) which is processed by ANN algorithms to predict failures, substituting simple mechanical gauges with intelligent computational systems that analyze multiple parameters simultaneously to improve reliability.
Solution Approach 2:
The transmission system performs self-diagnosis through integrated sensors and onboard processing. The neural network analyzes sensor data from the transmission itself to detect anomalies and predict maintenance needs, enabling the system to monitor its own health without external intervention, thereby improving reliability while keeping the added complexity minimal.
2Loss of time
If no predictive maintenance is implemented, then device complexity remains low, but loss of time increases due to unexpected failures
Solution Approach 1:
The neural network performs preliminary analysis of transmission data to predict failures before they occur. By continuously monitoring parameters like temperature, pressure, and vibrations, the system identifies trends indicating upcoming failures, allowing maintenance to be scheduled in advance rather than reacting to unexpected breakdowns, thus reducing downtime.
Solution Approach 2:
The system establishes a feedback loop where sensor data from the transmission feeds into the neural network, which generates predictions about future failures. These predictions feed back to maintenance scheduling systems, creating a continuous cycle of monitoring, analysis, and proactive maintenance that reduces unplanned downtime.
Data Source
AI summary
Systems, methods and apparatuses of predictive maintenance of automotive transmission of vehicles. For example, the transmission has at least one sensor to measure a temperature in transmission fluid, the torque applied on a shaft of the transmission, a vibration sensor, and/or a microphone. During a period in which the vehicle is assumed to be operating normally, the sensor data generated by the transmission sensor(s) is used to train an artificial neural network to recognize the normal patterns in the sensor data. Subsequently, the trained artificial neural network is used to determine whether the current sensor data from the transmission sensor(s) are abnormal. A maintenance alert can be generated for the vehicle in response to a determination that the operations of the transmission are abnormal according to the artificial neural network and the current sensor data.