Hydraulic Pump Signal Analysis for Subcomponent Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current hydraulic system maintenance practices are inefficient due to a 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 monitoring and analyzing hydraulic equipment at subcomponent and global levels, using a single set of sensors to detect performance issues and predict maintenance needs, with machine learning algorithms for data processing and notification of 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 are improved, but device complexity and loss of time increase
Solution Approach 1:
The patent applies universality by using a single set of sensors to monitor multiple hydraulic subcomponents (pump, motor, valves, actuators) simultaneously. The sensor system is designed to be multi-functional, capturing pressure, flow, and temperature data that can be analyzed to diagnose issues across the entire hydraulic system rather than requiring dedicated sensors for each component.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives raw sensor data and transforms it into component-specific diagnostic information. This intermediary system includes algorithms and processing units that interpret the sensor signals to identify subcomponent issues, effectively mediating between the simple sensor input and the complex diagnostic output without requiring complex sensors themselves.
2Reliability
If domain expertise resources are allocated for system diagnosis, then reliability and measurement precision improve, but loss of time and productivity decrease
Solution Approach 1:
The patent implements preliminary action by pre-programming diagnostic algorithms and expertise into the monitoring system. Instead of requiring technicians to apply domain expertise during troubleshooting, the system has this expertise built-in advance through embedded algorithms that automatically analyze sensor data and identify issues, providing immediate diagnostic results when problems occur.
Solution Approach 2:
The monitoring system applies self-service by automatically diagnosing hydraulic system issues without requiring external expert intervention. The embedded algorithms continuously analyze sensor data and can independently identify subcomponent failures, eliminating the need for technicians to spend time troubleshooting and enabling immediate maintenance actions.
3Device complexity
If reactive maintenance practices are followed, then device complexity is reduced, but reliability and productivity worsen due to accelerated system wear
Solution Approach 1:
The patent applies preliminary action by enabling predictive maintenance through continuous monitoring and analysis. The system detects early signs of component degradation and predicts potential failures before they occur, allowing maintenance to be performed proactively rather than reactively. This extends component life and prevents accelerated wear by addressing issues before they escalate.
Solution Approach 2:
The monitoring system implements feedback by continuously analyzing sensor data and providing real-time information about component health and system performance. This feedback loop enables operators to adjust operations or schedule maintenance based on actual component conditions rather than following fixed schedules, optimizing reliability while managing complexity through intelligent decision-making.
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.