Sensor Drift Analysis for Predictive Equipment Failure
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Solution Overview
Problem
Existing security and access control systems lack the ability to effectively predict equipment failures and anomalies, leading to inefficient maintenance and potential security breaches, as data is typically stored locally and not utilized for predictive analytics.
Innovation Solution
A computer program product that collects sensor data from multiple sensors, applies unsupervised learning models to determine normal and drift states, and predicts equipment failures or service needs, sending alerts for maintenance and insurance rate modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is stored locally in product memory or electronic logs, then data storage is simple and device complexity is low, but predictive analytics capability is lost and equipment failure cannot be predicted
Solution Approach 1:
The patent extracts the predictive analytics functionality from the local sensor devices and relocates it to a centralized cloud-based platform. Sensor data is collected locally but transmitted to the cloud where unsupervised learning models perform drift analysis and failure predictions. This extraction allows local devices to remain simple while gaining predictive capabilities through cloud processing.
Solution Approach 2:
The patent introduces a cloud-based intermediary platform that mediates between local sensor data collection and predictive analytics. The intermediary receives sensor data, applies unsupervised learning models, and returns predictions to users. This intermediary enables predictive capabilities without requiring complex local processing infrastructure.
2Productivity
If traditional monitoring systems are used, then system simplicity is maintained, but maintenance efficiency is low and security breaches may occur due to inability to predict failures
Solution Approach 1:
The patent implements preliminary action by continuously analyzing sensor data to detect drift states before equipment failure occurs. The unsupervised learning models identify abnormal patterns early in the equipment lifecycle, enabling proactive maintenance scheduling. This preliminary detection prevents the need for reactive emergency repairs and reduces downtime.
Solution Approach 2:
The patent establishes a feedback loop where sensor data continuously feeds into the unsupervised learning models, which generate predictions that are communicated back to users. This feedback mechanism enables ongoing monitoring and adjustment of maintenance schedules based on actual equipment conditions rather than fixed intervals.
3Loss of information
If data is only used for limited purposes locally, then data processing requirements are minimal, but valuable predictive information is lost
Solution Approach 1:
The patent makes the collected sensor data multi-functional by applying unsupervised learning models to extract multiple types of predictive information. The same data stream is analyzed for drift detection, failure prediction, and maintenance optimization. This universal processing approach maximizes the value extracted from the collected data without requiring separate data collection systems for each purpose.
Data Source
AI summary
Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.