Machine Sensor Pattern Forecasting for Early Failure Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine monitoring systems fail to accurately predict failures in advance, often requiring human intervention and relying on predetermined rules, leading to unnecessary maintenance, wasted resources, and prolonged downtime due to their inability to consider all relevant data and minute fluctuations in machine behavior.
Innovation Solution
A method and system for recognizing and forecasting anomalous sensory behavioral patterns in machines by monitoring time-stamped sensory input data, analyzing it using machine learning models, and comparing suspicious patterns to previously identified failure patterns to generate predictive notifications above a predetermined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing monitoring systems use predetermined rules and periodic testing, then they can identify failures, but they fail to predict failures in advance and require human intervention
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing sensory data patterns without human intervention. The machine learning model autonomously identifies suspicious patterns and generates notifications, eliminating the need for specialized operators to interpret sensor data and make maintenance decisions.
Solution Approach 2:
The patent replaces manual analysis of sensory data with an automated machine learning system. Instead of human operators examining sensor readings, the system uses algorithms to detect patterns, compare them against historical failure data, and generate predictions automatically.
2Reliability
If existing solutions rely on predetermined rules for monitoring, then they can check key parameters, but they ignore most collected data resulting in wasted computing resources
Solution Approach 1:
The system extracts only the relevant information from sensory data by using machine learning to identify suspicious patterns. Instead of processing all raw sensor data indefinitely, the system extracts meaningful patterns that indicate potential failures, comparing them against historical failure patterns to generate predictions.
3Reliability
If periodic testing is performed at predetermined intervals, then maintenance can be scheduled, but it results in premature replacement of functioning parts and wasted materials
Solution Approach 1:
The system performs preliminary detection of failure patterns by continuously analyzing sensory data and comparing it against historical failure data. This allows the system to predict failures before they occur, enabling maintenance to be scheduled at the optimal time rather than using fixed intervals, thus avoiding premature replacement of still-functioning parts.
4Measurement precision
If dedicated testing equipment and specialized operators are required, then monitoring can be performed, but it introduces human error and increases operational costs
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing sensory data patterns without human intervention. The machine learning model autonomously identifies suspicious patterns and generates notifications, eliminating the need for specialized operators to interpret sensor data and make maintenance decisions.
Data Source
AI summary
A system and method for recognizing and forecasting anomalous sensory behavioral patterns of a machine, including: monitoring a first set of time-stamped sensory input data related to at least one machine; determining, upon analysis of the first set of time-stamped sensory input data, a first suspicious pattern of a first anomalous sensory input behavior associated with the first set of time-stamped sensory input data; comparing the first suspicious pattern to a second pattern of a second anomalous sensory input behavior that is associated with a second set of time-stamped sensory input data, wherein the second pattern previously determined to be indicative of a machine failure; and, determining if the first suspicious pattern is correlated above a predetermined threshold with the second pattern.