Manufacturing Workflow Engine for Real-Time Anomaly Response
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Solution Overview
Problem
Manufacturing processes face challenges in detecting and addressing anomalies in real-time, leading to difficulties in product quality and throughput, particularly in batch manufacturing where errors can propagate and be difficult to handle effectively.
Innovation Solution
A workflow system that receives and compares time series values of sensed parameters to detect anomalies, using an analytics engine to recommend actions based on knowledge, rules, and operator skills, enabling dynamic intervention in batch processes and improving decision-making through data-driven approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and anomaly detection are implemented in manufacturing processes, then product quality and manufacturing throughput are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an analytics engine as an intermediary component that sits between the manufacturing execution system and the workflow management system. This engine aggregates time series data from multiple sensors, performs anomaly detection, and translates complex data patterns into actionable workflow triggers, thereby managing system complexity while enabling comprehensive quality monitoring
Solution Approach 2:
The system implements self-service through automated anomaly detection and workflow execution. The analytics engine continuously monitors sensor data, automatically detects anomalies without human intervention, and triggers appropriate workflows based on predefined rules, reducing the need for manual quality checking while maintaining high reliability
2Measurement precision
If comprehensive time series data collection and analysis are performed, then anomaly detection accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-defining anomaly detection rules, thresholds, and workflow responses before manufacturing operations begin. The system pre-processes time series data into aggregated metrics and pre-configures workflow triggers, enabling rapid real-time anomaly detection without extensive computational analysis during critical manufacturing moments
Solution Approach 2:
The system segments the manufacturing process into discrete workflow steps with specific monitoring parameters for each stage. By dividing comprehensive data collection into targeted, step-specific measurements, the system maintains high detection accuracy while reducing overall data processing requirements through focused, relevant data collection at each process segment
3Productivity
If dynamic workflow execution and automated control are implemented, then manufacturing throughput is improved, but difficulty in handling errors and exceptions increases
Solution Approach 1:
The patent implements feedback mechanisms where the analytics engine continuously monitors process parameters and provides real-time feedback to the workflow management system. When anomalies are detected, the system automatically adjusts workflow execution, triggers corrective actions, and monitors the effects, creating a closed-loop control system that maintains high throughput while systematically managing errors through automated feedback-driven adjustments
Data Source
AI summary
A system and method include receiving time series values of one or more sensed parameters of a product manufacturing processes. The received time series values are compared to desired time series values to detect one or more anomalies in the received time series values. An action to apply to the product manufacturing process product is determined based on the one or more detected anomalies is orchestrated by an intelligent workflow engine, that derives inferences from analytics, heuristics rules, skills, and a knowledge library.