Transmission failure, the cause could not be known in advance with
existing inspection methods
The damper clutch is a core component within the transmission, and when a failure occurs, it leads to power loss, abnormal vibration, and transmission shutdown, affecting the entire production line. The problem was that the pattern of CAN data varied depending on operating conditions, making it difficult to distinguish between normal and fault states. Even if a breakdown occurred, the exact cause of what type of defect it was could not be specified, leaving the person in charge in a situation where they had to rely on complete inspections.
From data classification by operating conditions to fault diagnosis algorithms, establishing a CAN data-based diagnosis system
We first established a system that collects normal and fault data while changing driving modes, and automatically classifies the data according to operating conditions. Afterward, we extracted core feature factors related to faults through frequency analysis and statistical analysis, and developed an algorithm that automatically diagnoses the type of damper clutch fault based on trend analysis for each classification. Through this, we realized a structure that enables consistent diagnosis even if operating conditions change.
Realization of automatic classification of damper clutch fault types and
an early diagnosis system
Automatic classification of fault types Data is automatically classified according to operating conditions, and it is now possible to accurately specify the fault type through trend analysis for each classification. The defective part can be quickly identified without the existing complete inspections. Early detection based on core factors Based on the core feature factors extracted through frequency and statistical analysis, detection is now possible in the early stages when abnormal behavior begins. Proactive measures can be taken before it leads to power loss or transmission shutdowns. Improved maintenance efficiency Unnecessary inspections due to unknown causes are reduced, and maintenance efficiency is increased because only necessary parts can be intensively maintained based on accurate fault diagnosis.
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existing inspection methods
The damper clutch is a core component within the transmission, and when a failure occurs, it leads to power loss, abnormal vibration, and transmission shutdown, affecting the entire production line. The problem was that the pattern of CAN data varied depending on operating conditions, making it difficult to distinguish between normal and fault states. Even if a breakdown occurred, the exact cause of what type of defect it was could not be specified, leaving the person in charge in a situation where they had to rely on complete inspections.
We first established a system that collects normal and fault data while changing driving modes, and automatically classifies the data according to operating conditions. Afterward, we extracted core feature factors related to faults through frequency analysis and statistical analysis, and developed an algorithm that automatically diagnoses the type of damper clutch fault based on trend analysis for each classification. Through this, we realized a structure that enables consistent diagnosis even if operating conditions change.
an early diagnosis system
Automatic classification of fault types Data is automatically classified according to operating conditions, and it is now possible to accurately specify the fault type through trend analysis for each classification. The defective part can be quickly identified without the existing complete inspections. Early detection based on core factors Based on the core feature factors extracted through frequency and statistical analysis, detection is now possible in the early stages when abnormal behavior begins. Proactive measures can be taken before it leads to power loss or transmission shutdowns. Improved maintenance efficiency Unnecessary inspections due to unknown causes are reduced, and maintenance efficiency is increased because only necessary parts can be intensively maintained based on accurate fault diagnosis.