Landing Gear Sensor Data Collection for Predictive Maintenance
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Solution Overview
Problem
Current maintenance practices in the aviation industry rely heavily on time-consuming visual inspections and manual data entry, which are prone to human error and do not account for the actual condition of aircraft components, leading to inefficient and costly maintenance cycles.
Innovation Solution
Implementing a system that collects and analyzes measurement data from landing gear components using sensors and edge nodes, which send signals to sensors to gather data on tire pressure, temperature, brake wear, and other parameters, and stores this data on an RFID card or remote server for predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual inspections are performed manually by pilots or technicians, then maintenance issues can be detected, but the process is time-consuming and susceptible to human perception limitations
Solution Approach 1:
The patent replaces manual visual inspection with an automated sensor-based measurement system. Sensors mounted on the aircraft continuously collect data on component conditions (vibration, temperature, acoustic emissions), eliminating the need for manual visual inspection and providing more reliable detection capabilities.
Solution Approach 2:
The measurement system enables the aircraft components to self-monitor their own condition. Sensors detect early signs of distress in tires, brakes, and other components, allowing the system to identify maintenance needs without human intervention and before failures occur.
2Loss of information
If manual entry procedures are used for storing maintenance data, then data can be recorded in computer systems, but the process is prone to human error and inefficiency
Solution Approach 1:
The patent replaces manual data entry with automated electronic data collection. Sensors continuously measure component conditions and automatically transmit data to a central system, eliminating manual keying errors and improving data accuracy while significantly increasing productivity.
Solution Approach 2:
The system establishes continuous feedback loops where sensors monitor component conditions in real-time, automatically update maintenance databases, and trigger alerts when maintenance thresholds are approached. This creates an automated information flow that eliminates manual intervention.
3Reliability
If routine maintenance is performed on a fixed schedule, then maintenance cycles can be standardized, but components may be replaced before actual wear occurs, increasing costs
Solution Approach 1:
The patent transitions from static, schedule-based maintenance to dynamic, condition-based maintenance. The system continuously monitors actual component conditions and adjusts maintenance timing based on real-time wear indicators, allowing maintenance to be performed precisely when needed rather than according to fixed schedules.
Solution Approach 2:
The measurement system detects early signs of component distress before actual failure occurs. By identifying trends in vibration, temperature, and acoustic emissions, the system enables proactive maintenance planning, allowing airlines to schedule maintenance during planned downtime rather than experiencing unscheduled delays.
Data Source
AI summary
Systems and methods are disclosed for data collection from landing gear components and providing predictive analytics. Example methods include sending a signal to excite wheel assembly sensors located on a vehicle, and receiving a return signal encoded with measurement data. The measurement data is then stored on an RFID card, on an edge node, and/or on a remote server. Upon receipt of the measurement data by a remote server, the remote server analyzes the data to generate predictive maintenance analytics data.