Hydraulic Event Monitoring Using Unified Sensor Signal Analysis
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Solution Overview
Problem
Current hydraulic system monitoring technologies are inefficient and costly, as they require extensive resources and domain expertise to diagnose subcomponent issues, leading to reactive maintenance practices that accelerate system wear and increase downtime.
Innovation Solution
A sensor system and platform for signal analysis that allows for monitoring, forecasting, and troubleshooting of hydraulic equipment at both global and subcomponent levels, using a single set of sensors and advanced algorithms to process signal streams and identify unique signatures corresponding to system states and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If distributed sensors and custom algorithms are used for full system monitoring, then measurement precision and reliability improve, but device complexity and cost increase
Solution Approach 1:
The patent consolidates multiple sensor types (pressure, temperature, flow) into a unified monitoring system that processes signals through a single platform. This merging approach maintains comprehensive monitoring capabilities while reducing system complexity by eliminating the need for separate distributed sensor networks and multiple custom algorithms.
Solution Approach 2:
The monitoring system is designed to perform multiple functions using a single set of sensors and processing platform. It can diagnose subcomponent issues, monitor global system states, generate maintenance forecasts, and provide troubleshooting guidance, thereby reducing device complexity while maintaining measurement precision through multi-functional capability.
2Measurement precision
If application-specific domain expertise and resources are deployed, then measurement precision and reliability improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The system incorporates automated diagnostic capabilities that generate maintenance forecasts and troubleshooting guidance without requiring external domain expertise for each diagnostic task. The platform processes sensor data and automatically identifies subcomponent issues, reducing implementation difficulty while maintaining diagnosis precision through self-service automation.
Solution Approach 2:
The system implements feedback loops where diagnostic results and maintenance forecasts are continuously refined based on system performance data. This feedback mechanism enables the platform to improve diagnosis precision over time while maintaining ease of implementation through automated learning and adaptation rather than requiring ongoing expert intervention.
3Device complexity
If reactive maintenance practices are followed, then device complexity remains low, but reliability and productivity deteriorate
Solution Approach 1:
The system generates maintenance forecasts that identify potential failures before they occur, enabling proactive maintenance scheduling. This preliminary action approach improves equipment availability and reliability by preventing unexpected breakdowns while maintaining manageable system complexity through automated forecasting algorithms.
Solution Approach 2:
The system replaces manual reactive maintenance practices with automated monitoring and forecasting algorithms. This substitution improves reliability by continuously monitoring system states and predicting failures, while maintaining low complexity through the use of standardized processing platforms and automated decision-making algorithms.
4Productivity
If component replacement is performed without root cause analysis, then productivity recovers quickly, but reliability deteriorates due to accelerated wear
Solution Approach 1:
The system provides feedback on root cause diagnoses alongside maintenance forecasts, enabling informed component replacement decisions. This feedback mechanism allows productivity to be recovered through timely maintenance while preventing accelerated wear by addressing underlying causes rather than merely replacing components, thereby improving both productivity and reliability.
Solution Approach 2:
The monitoring system acts as an intermediary between system operation and maintenance decisions. It analyzes sensor data to identify root causes of potential failures and provides guided recommendations, enabling productivity recovery through targeted maintenance while preventing reliability deterioration by eliminating underlying issues rather than just replacing components.
Data Source
AI summary
A system includes sensors for monitoring signals, and a processing system executes one or more methods for identification of system events, from the signals, corresponding to state changes and performance of the system and/or its subcomponents. Event identification is performed with classification and/or other machine learning algorithms, with generation of novel training data sets. The sensor(s) can also be used to determine power consumption information about the system and/or its subcomponents. The system processes event-associated outputs for execution of actions for improving system performance, along with other downstream applications.