Method for Diagnosis and Monitoring of Vehicles

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This patent describes a method and system for diagnosing and monitoring vehicles, especially rail vehicles, using one or more acoustic (airborne sound) sensors. The system captures sound data from vehicle components, extracts relevant acoustic features (such as cepstral coefficients, MFCCs, or other audio-derived parameters), and uses these features to perform automated diagnostics and condition monitoring—often relying on machine learning models, such as neural networks, for fault detection. This allows for accurate identification and localization of component faults or abnormal states based on sound signature analysis.

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• Continuous real-time monitoring and diagnosis of railway vehicle components (e.g., wheels, bearings, gears, suspension) during operation.
• Condition-based maintenance scheduling for trains, reducing unnecessary routine checks and optimizing maintenance intervals.
• Automated detection of faults such as wheel flats, bearing defects, or gear/motor issues from acoustic signatures.
• Remote health monitoring of fleets, with data transmitted from trains to centralized maintenance facilities for evaluation and planning.
• Onboard real-time warning to train operators about emerging or critical faults to enhance safety and reduce breakdowns.
• Post-trip diagnostic analysis for maintenance teams, using stored acoustic data to identify deterioration trends.

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• Non-intrusive: Sensors need not be directly attached to components; placement flexibility allows for monitoring multiple parts with fewer sensors.
• Cost-effective: Reduces the number of required sensors and associated hardware, compared to traditional approaches like accelerometers.
• Enhanced fault detection: Acoustic analysis can distinguish between different failure modes more effectively than vibration-only monitoring.
• Supports predictive, condition-based maintenance instead of fixed schedules, saving costs and improving fleet availability.
• Adaptable: Employs machine learning methods (e.g., neural networks) that can be trained for specific vehicles, components, and environments.
• Enables remote diagnostics and faster response, as data can be relayed to central infrastructure for timely maintenance actions.
• Improves safety and operational reliability by detecting faults early and providing actionable alerts or maintenance recommendations.

Technical Classifications (CPCs)

Inventors & Applicants

Patent Abstract

The invention relates to a method for diagnosing and monitoring vehicles, in particular a drive system of a rail vehicle, at least having the following steps: a) receiving an acoustic airborne sound signal by means of at least one acoustic sensor (1, 2, 3, 4, 5, 6, 7, 8); b) determining at least one feature from the airborne sound signal; and c) carrying out at least one step selected from a diagnosis and a monitoring process of at least one vehicle component on the basis of at least one feature determined in step b).