Predictive Maintenance Using Sensor Data and Weather

Solution Overview

Problem

Conventional maintenance programs require frequent and often unexpected inspections and replacements of vehicle components, leading to unanticipated breakdowns, increased costs, and equipment downtime, as they lack predictive capabilities based on real-time data and environmental factors.

Innovation Solution

A predictive maintenance system that wirelessly collects and analyzes data from on-board sensors, historical maintenance records, and weather conditions to anticipate when maintenance is needed, using statistical models and machine-learning algorithms to generate accurate maintenance schedules, thereby enabling preemptive action.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional maintenance programs are used, then equipment reliability is maintained through frequent inspections, but productivity decreases due to unexpected breakdowns and equipment downtime

Engineering Contradiction:

Improveequipment reliabilityVSAvoidproductivity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary maintenance actions by predicting future equipment failures before they occur. Sensors continuously monitor equipment parameters and the predictive algorithm generates maintenance recommendations in advance, allowing operators to schedule maintenance during planned downtime rather than experiencing unexpected breakdowns that disrupt productivity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback loops where sensor data from equipment is continuously collected, analyzed by predictive algorithms, and used to generate maintenance recommendations. This feedback mechanism allows the system to learn from actual equipment behavior and improve prediction accuracy over time, balancing reliability maintenance with productivity optimization

Inventive Principle:

Principle #23Feedback

2Reliability

If frequent inspections are performed, then equipment reliability is improved, but loss of time increases due to maintenance interruptions

Engineering Contradiction:

Improveequipment reliabilityVSAvoidloss of time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary maintenance actions by predicting future equipment failures before they occur. Sensors continuously monitor equipment parameters and the predictive algorithm generates maintenance recommendations in advance, allowing operators to schedule maintenance during planned downtime rather than experiencing unexpected breakdowns that disrupt productivity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transitions from static scheduled maintenance to dynamic condition-based maintenance. Maintenance intervals and recommendations are continuously adjusted based on real-time sensor data and predictive algorithm outputs, allowing the system to optimize the balance between reliability and time loss by performing maintenance only when actually needed

Inventive Principle:

Principle #15Dynamics

3Reliability

If conventional maintenance schedules are followed, then equipment reliability is maintained, but loss of substance increases due to premature component replacement

Engineering Contradiction:

Improveequipment reliabilityVSAvoidloss of substance

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The system performs preliminary maintenance actions by predicting future equipment failures before they occur. Sensors continuously monitor equipment parameters and the predictive algorithm generates maintenance recommendations in advance, allowing operators to schedule maintenance during planned downtime rather than experiencing unexpected breakdowns that disrupt productivity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes maintenance decision parameters from fixed time-based schedules to dynamic condition-based thresholds. By monitoring actual equipment parameters such as vibration, temperature, and performance metrics, the system determines maintenance needs based on real equipment state rather than predetermined time intervals, preventing premature replacement of still-functional components

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11449838B2Predictive maintenance

Publication Date: 2022.09.20 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.