Engine Accessory Prognostics via Intermittent Load Variation
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Solution Overview
Problem
Conventional diagnostic and prognostic methods for rotary engine accessories with intermittent duty cycles fail to account for real-world operational demands, leading to unreliable predictions of component failure and increased costs due to high failure rates, particularly in commercial vehicles where air-conditioner compressors experience frequent failures.
Innovation Solution
A diagnostic and prognostic system utilizing an electronic control system with health monitoring subsystems, including data set richness metrics and strategic system identification processes, to predict the health of rotary engine accessories by varying operational conditions and collecting diverse data sets, enabling timely warnings and minimizing component damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional diagnostic approaches are used for rotary engine accessories with intermittent duty cycles, then the system structure remains simple, but the reliability of failure prediction is poor
Solution Approach 1:
The diagnostic system is segmented into multiple independent functional modules: data collection module, data set richness evaluation module, system identification module, and prognostics module. Each module performs a specific function, allowing the complex diagnostic task to be divided into manageable segments that can be executed sequentially, thereby improving reliability without overwhelming system complexity.
Solution Approach 2:
The system performs preliminary actions by collecting and evaluating data set richness metrics before attempting prognostics. The system identifies optimal operating conditions in advance and prepares diverse data sets beforehand, ensuring that reliable failure predictions can be made when sufficient data is available, rather than attempting predictions with insufficient data.
2Measurement precision
If diverse data sets are collected under varying operational conditions, then the measurement precision of component health improves, but the loss of time for data collection increases
Solution Approach 1:
The system dynamically adjusts the data collection strategy based on real-time evaluation of data set richness metrics. When sufficient diverse data has been collected under varying operational conditions, the system transitions from data collection mode to prognostics mode, eliminating the need for continuous data collection and thereby reducing time loss while maintaining high measurement precision.
Solution Approach 2:
The system continuously monitors and evaluates data set richness metrics as feedback, using this information to determine when sufficient data has been collected. This feedback mechanism allows the system to stop data collection once precision requirements are met, preventing unnecessary time loss while ensuring adequate measurement precision for reliable prognostics.
3Measurement precision
If the system monitors engine load values during rotary load engagement and disengagement, then the diagnostic accuracy improves, but the device complexity increases
Solution Approach 1:
The electronic control system performs multiple functions: it controls engagement/disengagement of the rotary load, monitors engine load values, evaluates data set richness, and performs prognostics. By making the control system universal and multi-functional, the patent avoids adding separate dedicated monitoring hardware, thereby improving diagnostic accuracy without proportionally increasing device complexity.
Data Source
AI summary
One embodiment is a system comprising an engine structured to output torque to an accessory drive, a rotary load structured to be selectably driven by the accessory drive, and an electronic control system. The electronic control system is operable to selectably engage and disengage the rotary load effective to vary the load on the engine, monitor engine load values in coordination with engagement of the rotary load, store a data set comprising the monitored engine load values in association with values of one or more associated system conditions in a non-transitory memory medium, update a mathematical model of the system stored in the non-transitory memory medium in response to the engine load values to converge one or more model parameters, diagnose or prognosticate a failure state of the rotary load in response to a change in the one or more model parameters, and output a perceptible diagnostic indication of the failure state in response to the diagnosis or prognostication.