Double-Blind Prognostic Surveillance Evaluation via Synthetic Fault Injection
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Solution Overview
Problem
Existing prognostic-surveillance systems face challenges in performing fair comparisons due to legal privacy restrictions and the difficulty in obtaining shared datasets, as well as the potential for unfair advantages when inserting hidden faults, which complicates the evaluation of competing systems.
Innovation Solution
A double-blind comparison method is implemented, where a local and remote system independently insert random faults into a dataset and exchange encrypted fault information, allowing each system to analyze the other's seeded dataset and compare performance without knowing the other's fault configurations, thereby ensuring a fair evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single system inserts hidden faults into the data to evaluate competing prognostic-surveillance techniques, then the evaluation can be performed, but one system may gain an unfair advantage by configuring its ML system to more carefully examine the places where the hidden faults are located
Solution Approach 1:
The fault insertion process is segmented and distributed across multiple independent systems. Each system independently inserts faults into copies of the dataset, ensuring that no single system knows the fault locations in the other system's data. This segmentation eliminates the unfair advantage while maintaining evaluation reliability.
Solution Approach 2:
An intermediary mechanism is introduced where systems exchange encrypted fault information. The intermediary encryption layer ensures that systems can verify fault detection without knowing the actual fault locations, preventing any system from optimizing its detection based on known fault positions.
2Measurement precision
If real data is used to evaluate competing prognostic-surveillance techniques, then the evaluation reflects real-world performance, but data privacy issues make it difficult for customers to provide real data
Solution Approach 1:
Instead of using actual real data that could reveal sensitive information, the system creates synthetic copies of datasets that preserve the structural characteristics and statistical properties of real data without containing actual sensitive information. This allows evaluation accuracy to be maintained while data privacy is protected.
Solution Approach 2:
The data is transformed by changing its parameters - synthetic data is generated that matches the statistical distribution and structural characteristics of real data but lacks actual sensitive values. This parameter transformation enables evaluation accuracy while eliminating data privacy concerns.
3Reliability
If it takes a significant amount of time to obtain legal permissions to exchange ML codes with a customer, then legal privacy restrictions must be respected, but the evaluation process becomes inefficient
Solution Approach 1:
The evaluation framework is designed with preliminary actions that establish clear legal and technical boundaries before data exchange begins. By pre-defining how data is encrypted, synthetic, and exchanged, the system reduces the time needed for legal permissions while maintaining compliance.
Solution Approach 2:
An intermediary legal and technical framework is introduced that mediates between data privacy requirements and evaluation needs. This intermediary structure allows rapid setup of compliant evaluations by providing pre-established protocols for data exchange that satisfy legal requirements without requiring lengthy case-by-case permissions.
Data Source
AI summary
A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.