Hydraulic Signal Analysis for Subcomponent Event Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current hydraulic system maintenance practices are inefficient due to the lack of domain expertise and resources, leading to reactive maintenance and accelerated system wear, resulting in costly downtime and reduced production output.
Innovation Solution
A sensor system and platform for signal analysis that monitors hydraulic equipment at subcomponent and global levels, using a single set of sensors to provide real-time monitoring, forecasting, and troubleshooting capabilities, enabling predictive maintenance and extended equipment lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If many distributed sensors and custom algorithms are used for full system monitoring, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent applies universality by using a single set of sensors to monitor multiple hydraulic subcomponents (pump, motor, valves, actuators) through signal disaggregation. Instead of deploying dedicated sensors for each component, the system processes signals from one sensor location to extract information about the entire hydraulic system, reducing device complexity while maintaining monitoring precision.
Solution Approach 2:
The patent applies segmentation by dividing the hydraulic system into subcomponent levels (pump, motor, valves, actuators) and using machine learning models to separately analyze signals for each subcomponent. This allows precise monitoring of individual components through a unified sensor system, resolving the contradiction between measurement precision and system complexity.
2Productivity
If reactive maintenance practices are used without proper diagnostic tools, then device complexity is reduced, but productivity and reliability deteriorate due to equipment downtime
Solution Approach 1:
The patent applies preliminary action by implementing predictive maintenance that identifies potential failures before they occur. The system continuously monitors hydraulic parameters and uses machine learning to forecast equipment issues, allowing maintenance to be performed proactively rather than reactively, thus maintaining productivity without requiring complex diagnostic expertise from operators.
Solution Approach 2:
The system applies self-service by automatically analyzing hydraulic system data and generating diagnostic insights without requiring external expert intervention. The machine learning models process sensor data and provide maintenance recommendations autonomously, enabling equipment operators to perform predictive maintenance without needing specialized domain expertise, thereby maintaining productivity while managing system complexity.
3Reliability
If components are replaced immediately upon failure, then reliability is restored, but loss of time and productivity increase due to downtime
Solution Approach 1:
The patent applies preliminary action by predicting component failures before they occur using machine learning analysis of sensor data. The system identifies early signs of degradation and schedules maintenance during planned downtime rather than waiting for actual failure, thereby maintaining equipment availability while minimizing unplanned downtime and production loss.
Data Source
AI summary
A system includes sensors for monitoring pressure, flow, pump speed, temperature, and/or other signals at the output of a main hydraulic pump, and a processing system executes one or more methods for identification of hydraulic 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.