Machine Part Testing TRIZ Cases
- Inductive Keystroke Detection for Wear-Free Variable Key Input
An inductive resonance circuit replaces mechanical contacts to avoid wear while enabling adjustable switching points and variable key signals.
- Vehicle Diagnostic Control for Torque-Based Fault Override
An independent diagnostic controller uses input state and motor torque to detect faults and override commands that could cause unintended acceleration.
- Undercarriage Wear Prediction Using Machine Learning
Machine learning combines sensor, inspection, and simulation data to predict undercarriage wear and support timely maintenance.
- Internal Crankshaft Monitoring for Speed Reducer Fault Prediction
Multiple internal crankshaft sensors compare vibration, temperature, and rotation signals to improve speed reducer fault prediction.
- Drivetrain Test Stand Coupling With Tire-Like Torsional Damping
A friction-coupled wheel and cap module adds tire-like elasticity to drivetrain test stands, improving bench-to-vehicle behavior accuracy.
- Multi-Sensor Signal Fusion for Non-Stationary Machine Health Analysis
Fused non-stationary sensor signals and wavelet-based features improve machine health assessment accuracy during changing operating conditions.
- Ball Screw Vibration Monitoring for Noise-Resistant Wear Detection
Averaging vibration data across two periods filters plant noise, improving ball screw pre-load and wear determination accuracy.
- Machine Fault Detection Using Neural Signal Extraction
An unsupervised neural network isolates impulsive fault signals from noisy machine vibration data, enabling clearer envelope spectrum diagnosis.
- Open Gear Health Prediction Using Pinion Inspection Data
Uses pinion gear inspection, imaging, and machine learning under full load to predict girth gear wear and plan maintenance.
- Gear Measurement Scope Based on Rolling Test Results
Rolling test results set component-specific gear measurement scope, cutting tactile inspection time while preserving noise control and quality assurance.
- Bearing RUL Estimation Using Vibration Spectrum and Virtual Models
Real-time vibration analysis and virtual bearing models estimate defect impact force and remaining life to cut downtime and safety risk.
- Abnormality Degree Calculation Using Device-Specific Normal Distributions
Distinguishing device-specific normal distributions improves anomaly scoring accuracy when normal data is limited and similar devices behave differently.
- Condition Monitoring Analysis Timing for Real-Time Anomaly Detection
Measurement and analysis intervals are set from processing time so condition monitoring stays real time and avoids data backlog.
- Coating Vibration Monitoring for Multi-Fault Diagnosis
Vibration signal analysis distinguishes imbalance, bearing, gearbox, and collision faults in coating equipment for earlier maintenance.
- Ultrasonic Bearing Lubrication Detection With Dynamic Gain Control
Dynamic gain control and heterodyning let ultrasonic sensors detect initial lubricant contact and track bearing lubrication without manual tuning.
- Control Rod Drive Motor Current Analysis for Abnormality Detection
Phase-current frequency analysis uses motor and mechanical natural frequencies to detect control rod drive abnormalities even without strong resonance.
- Remaining Useful Life Estimation Using Neural RUL Distributions
Neural estimation of RUL distribution parameters improves worn-part life prediction by learning from condition monitoring data and censored records.