IoT Conveyance Predictive Maintenance With Fuel-Gauge Visualization
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Solution Overview
Problem
Existing IoT-based predictive maintenance systems lack effective visualization of complex machine learning analytics, leading to inefficiencies in operational deployment and inability to handle large volumes of data, particularly in machinery like pneumatic conveying systems.
Innovation Solution
A predictive maintenance IoT system that receives sensor data, classifies it using a machine learning engine, and maps the predictive maintenance state onto a user-friendly interface, employing a combination of database architecture, data training architecture, and base-lining algorithms to predict maintenance needs and visualize results through a simple 'fuel gauge' representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning analytics are used for predictive maintenance, then measurement precision and reliability are improved, but device complexity and ease of operation deteriorate due to difficulty in visualizing and interpreting results
Solution Approach 1:
The patent introduces a visualization layer that acts as an intermediary between complex machine learning analytics and end users. This layer translates sophisticated algorithmic outputs into intuitive visual representations (charts, graphs, color-coded indicators) that maintain the precision of complex analytics while making results easily interpretable for operators without specialized data science knowledge
Solution Approach 2:
The patent creates simplified copies or representations of complex analytics results through visual interfaces. Instead of presenting raw computational outputs, the system generates visual copies (dashboard displays, trend visualizations, alert indicators) that convey the essential information from complex machine learning models in an accessible format
2Productivity
If traditional maintenance scheduling is used, then ease of operation is maintained, but productivity and loss of time increase due to unnecessary maintenance and unexpected failures
Solution Approach 1:
The patent implements predictive maintenance that performs preliminary actions by analyzing sensor data and machine learning models to predict future equipment failures before they occur. This allows maintenance to be scheduled proactively based on actual equipment condition rather than following fixed schedules, preventing unexpected breakdowns and optimizing maintenance timing to minimize operational disruption
Solution Approach 2:
The system establishes continuous feedback loops where sensor data from equipment is constantly monitored, analyzed by machine learning models, and used to update maintenance predictions. This feedback mechanism enables dynamic adjustment of maintenance schedules based on real-time equipment health status, allowing operators to respond to actual conditions rather than following rigid predetermined schedules
Data Source
AI summary
A method and system of a predictive maintenance IoT system comprises receiving a plurality of sensor data over a communications network and determining one or more clusters from the sensor data based on a pre-determined rule set. Further, the sensor data is classified through a machine learning engine and the sensor data is further base-lined through a combination of database architecture, data training architecture, and a base-lining algorithm. Intensity or degree of fault state is mapped to a fuel gauge to be depicted on a user interface and a predictive maintenance state is predicted through a regression model and appropriate alarm is raised for user action.