Prognostic Apparatus for Processing Equipment Using Heterogeneous Data Adjustment
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Solution Overview
Problem
Existing prognostic and health management technologies for processing apparatuses are inadequate for small-volume production with multiple product types, failing to accurately predict health changes and reduce unexpected downtime and maintenance frequency.
Innovation Solution
A prognostic method and apparatus that obtain multiple sensor data and heterogeneous data from processing apparatus components, calculate health indicators, and use regressive characteristic and adjustment functions to create a prediction function for estimating component usage status, thereby predicting potential failures and optimizing maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prognostic technologies are used for processing apparatus, then the system structure remains simple, but the prediction accuracy for small-volume multi-product production is insufficient
Solution Approach 1:
The prognostic system is segmented into multiple independent modules: data acquisition module for collecting sensor data, health indicator calculation module for processing sensor data into health indicators, adjustment function generation module for creating product-type-specific adjustment functions, and prediction module for generating failure predictions. This modular segmentation enables the system to handle small-volume multi-product production accurately while maintaining manageable system complexity through independent, reusable components.
Solution Approach 2:
The system dynamically changes parameters by generating different adjustment functions based on product types. The adjustment function module selects or creates specific adjustment functions corresponding to different product types being produced by the processing apparatus. This parameter adaptation allows the same base system to accurately predict failures across multiple product types without requiring complete system redesign for each product.
2Reliability
If traditional sensor-based maintenance is used, then the maintenance process is simple, but unexpected downtime cannot be effectively reduced
Solution Approach 1:
The system performs preliminary actions by continuously calculating health indicators from sensor data and generating failure predictions before actual failures occur. The prediction module identifies potential failures in advance, allowing maintenance to be scheduled proactively rather than reactively. This preliminary detection and prediction capability significantly reduces unexpected downtime while the modular architecture keeps system complexity manageable.
Solution Approach 2:
The system implements feedback mechanisms where health indicators are continuously calculated from sensor data, compared against prediction models, and used to generate updated failure predictions. The adjustment functions are refined based on actual operational data and prediction accuracy, creating a closed-loop system that improves reliability over time while maintaining efficient operation through automated feedback processing.
3Measurement precision
If generic prognostic models are applied to multiple product types, then the system remains simple to operate, but prediction accuracy for specific product types deteriorates
Solution Approach 1:
The prognostic system achieves universality by designing a multi-functional adjustment function module that can handle multiple product types through a unified interface. The module automatically selects or generates appropriate adjustment functions based on the current product type being produced, allowing the same system structure to serve multiple product lines accurately. This universal design maintains ease of operation while achieving product-type-specific prediction accuracy.
Solution Approach 2:
The adjustment function acts as an intermediary layer between the generic sensor data acquisition system and the specific product-type prediction requirements. This intermediary module translates general sensor data into product-specific health indicators by applying appropriate adjustment functions, enabling accurate predictions for different product types without requiring separate complete prognostic systems for each product.
Data Source
AI summary
A prognostic method and a prognostic apparatus for a processing apparatus are provided. In the steps of the prognostic method, multiple sensor data of a component of the processing apparatus and a heterogeneous data are obtained, multiple health indicators of the component are obtained by the multiple sensor data, a regressive characteristic function and an adjustment function are obtained according to the health indicators, the adjustment function corresponds to the heterogeneous data, a prediction function of health indicator is obtained according to the regressive characteristic function and the adjustment function, and a predictive value of health indicator is obtained according to the prediction function of health indicator to estimate a usage status of the component.