Fault Prediction System Using Multi-Sensor Data Validation
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Solution Overview
Problem
Existing monitoring and diagnostic services fail to detect equipment failures before they occur due to inefficient data analysis techniques, leading to unplanned outages and reduced reliability in fault prediction methods.
Innovation Solution
A system and method that receive on-site monitoring data from sensors, perform data validation, outlier analysis, and statistical distribution analysis, combine information from multiple sensors, and establish a prediction model to quantify the probability of faults in assets, enabling accurate fault prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing monitoring and diagnostic services use traditional data analysis techniques, then the system is simple to operate, but fault detection reliability is poor and false alarms increase
Solution Approach 1:
The patent segments the fault detection process into multiple independent modules: data collection module, data processing module (with validation, outlier analysis, filtration, imputation), feature extraction module, prediction model module, and result interpretation module. Each module handles a specific aspect of the analysis, improving reliability through specialized processing while organizing complexity into manageable segments.
Solution Approach 2:
The patent performs preliminary data processing actions before fault prediction: data validation checks data quality, outlier analysis identifies anomalous values, data filtration removes noise, and data imputation fills missing values. These preliminary actions prepare the data in advance, ensuring high-quality input for the prediction model and reducing false alarms.
2Measurement precision
If multiple sensors are integrated for comprehensive monitoring, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges data from multiple sensors (vibration sensors, temperature sensors, pressure sensors, flow sensors) into a unified analysis framework. The data processing module integrates these diverse data streams, applying consistent validation and processing rules to each, thereby improving measurement precision through multi-sensor correlation while managing integration complexity through standardized processing procedures.
Solution Approach 2:
The patent creates a universal data processing framework that handles multiple sensor types with a single integrated system. The same processing steps (validation, outlier analysis, filtration, imputation) are applied universally to data from different sensor sources, enabling multi-functional monitoring while avoiding the need for separate processing pipelines for each sensor type.
3Reliability
If advanced data processing techniques are applied, then false alarms are reduced, but processing time increases
Solution Approach 1:
The patent applies partial processing actions based on data quality assessment: data validation performs selective checking based on predefined thresholds, outlier analysis applies targeted identification rather than exhaustive examination, and data filtration uses efficient algorithms that process only necessary data points. This partial action approach maintains high prediction accuracy while reducing unnecessary processing time.
Data Source
AI summary
Systems and methods for fault prediction are described to reduce equipment failure by effectively monitoring equipment, removing anomalous data, and reducing false alarms. Such systems and methods can be used to receive monitoring data, extract information from the data, and combine extracted information for establishing prediction models. Additionally, fault probabilities may be quantified and faults may be predicted based on the probabilities.