Substrate Processing Sensor Health Index for Early Fault Response
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Solution Overview
Problem
Industrial substrate processing systems face challenges in timely detection and response to abnormal operating conditions, leading to manufacturing downtime and increased economic losses due to delays in identifying and addressing issues, which are exacerbated by the complexity of the systems and the difficulty in locating and diagnosing sensor-related problems.
Innovation Solution
A health monitoring, assessing, and response system that includes an interface and a controller with a health index module, which monitors sensor signals, determines health index values, and performs countermeasures based on these values, allowing for adaptive adjustment of thresholds and real-time assessment of system health, thereby reducing downtime and improving productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarm limits are applied to monitor parameters and halt operation when exceeded, then machine safety and defect prevention are improved, but manufacturing downtime and productivity are reduced due to time lag in detection and response
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing sensor data to detect trends indicating potential failures before they occur. The health index module calculates degradation trends and predicts future failures, allowing maintenance to be scheduled proactively rather than reactively, thus preventing downtime while maintaining safety.
Solution Approach 2:
The system dynamically adjusts monitoring based on real-time conditions. The health index calculation adapts to changing operational states, and the system transitions from static alarm thresholds to dynamic predictive thresholds that evolve with equipment aging, optimizing both safety and productivity by reducing false alarms while maintaining early failure detection.
2Reliability
If traditional alarm systems are used to detect abnormal conditions, then unacceptable operating conditions are identified, but additional downtime is lost to assess and understand the cause of the alarm
Solution Approach 1:
The health index module serves as an intermediary that continuously processes sensor data and translates complex multi-sensor information into simple, actionable health assessments. This intermediary layer pre-analyzes data trends and prepares diagnostic information in advance, so when an alarm occurs, the cause is already identified and presented to operators, eliminating diagnostic delays.
Solution Approach 2:
The system replaces manual diagnostic procedures with automated health index calculations and trend analysis. Instead of operators manually assessing alarm causes, the system automatically computes health indices from multiple sensors, identifies degradation patterns, and presents diagnostic conclusions, substituting mechanical human analysis with automated computational diagnostics.
3Measurement precision
If more sensors are deployed to improve monitoring coverage, then system health assessment is improved, but device complexity and difficulty of diagnosing sensor-related problems increase
Solution Approach 1:
The system merges data from multiple sensors into a unified health index calculation framework. Instead of treating each sensor independently, the health index module combines information from multiple sensors through weighted aggregation and trend analysis, creating a consolidated view of system health that reduces complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The health index module serves as a universal processing unit that handles data from various sensor types uniformly. It applies the same health index calculation methodology across different sensor inputs, creating a multi-functional framework that simplifies the handling of diverse sensor data while maintaining precise health assessment capabilities.
4Loss of time
If alarm thresholds are set close to normal operating limits, then early detection of degradation is improved, but false alarms increase leading to reduced productivity
Solution Approach 1:
The system performs preliminary trend analysis to distinguish between temporary fluctuations and genuine degradation trends. By analyzing the direction and rate of change in sensor readings over time, the system can set lower thresholds that trigger early warnings for true degradation while filtering out normal operational variations, reducing false alarms while maintaining early detection capability.
Solution Approach 2:
The system dynamically adjusts monitoring sensitivity based on operational context and historical data. Thresholds and alert levels adapt to changing operational conditions and equipment aging patterns, allowing the system to maintain high sensitivity for early detection during critical phases while reducing false alarm sensitivity during normal variability periods, optimizing both detection time and productivity.
Data Source
AI summary
A health monitoring, assessing and response system includes an interface and a controller. The interface is configured to receive a signal from a sensor disposed in a substrate processing system. The controller includes a health index module. The health index module is configured to perform an algorithm including: obtaining a window and a boundary threshold; monitoring the signal output from the sensor; determining whether the signal has crossed the boundary threshold; updating a health index component, where the health index component is a binary value and transitioned between HIGH and LOW values in response to the signal crossing the boundary threshold; and generating a health index value based on the health index component and decreasing the health index value from 100% to 0% over a duration of at least the window. The controller is configured to perform a countermeasure based on the health index value.