Industrial Equipment Audio Monitoring for Non-Contact Fault Detection
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Solution Overview
Problem
Existing methods for monitoring industrial equipment, such as HVAC systems, rely on invasive vibration sensors that require direct contact and are equipment-specific, leading to inefficiencies in fault detection and correction, often resulting in unpredictable downtime and manual intervention.
Innovation Solution
The use of non-invasive audio sensors that can detect faults without direct contact, allowing for proactive and automatic fault detection and correction, with the ability to learn noise profiles and fault patterns over time through crowd sourcing, enabling efficient monitoring across various equipment types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vibration sensors are used to detect faults in equipment, then fault detection capability is improved, but the sensors require direct contact with equipment and are equipment-specific, increasing device complexity and reducing adaptability
Solution Approach 1:
The patent replaces mechanical vibration sensors with acoustic sensors that detect faults through sound waves in the air. This substitution eliminates the need for direct contact with equipment, allowing the same acoustic sensor to monitor multiple types of equipment without physical attachment, thereby improving adaptability while maintaining fault detection capability
Solution Approach 2:
The acoustic sensor system is designed to be universal and equipment-agnostic, capable of monitoring various types of industrial equipment including HVAC systems, generators, and compressors. The system extracts multiple features from audio signals and uses machine learning models that can be trained on data from different equipment types, enabling one sensor system to serve multiple functions across diverse equipment
2Measurement precision
If vibration sensors are installed on equipment, then fault detection accuracy is improved, but installation and calibration require manual intervention and downtime, reducing productivity
Solution Approach 1:
By replacing contact-based vibration sensors with non-contact acoustic sensors, the system eliminates installation and calibration downtime. The acoustic sensors can be positioned remotely and begin monitoring immediately without requiring equipment shutdown or complex setup procedures, thus maintaining high measurement precision while maximizing equipment productivity
Solution Approach 2:
The system incorporates automatic feature extraction and machine learning-based fault detection that requires minimal manual calibration. The model can adapt to different equipment types through automated training processes, reducing the need for expert intervention and manual configuration, thereby improving productivity while maintaining accurate fault detection
3Measurement precision
If expert knowledge is centralized for fault diagnosis, then diagnostic accuracy is improved, but manual intervention and knowledge transfer time increase, reducing productivity
Solution Approach 1:
The system implements a feedback mechanism where fault data and diagnostic outcomes from multiple facilities are continuously collected and used to retrain and improve the machine learning models. This creates a self-improving system that automatically incorporates expert knowledge and field data, maintaining high diagnostic accuracy while eliminating manual knowledge transfer time through automated learning loops
Solution Approach 2:
The system creates digital copies of expert knowledge by training machine learning models on datasets containing expert diagnostic decisions and fault patterns. These trained models can be deployed across multiple facilities, effectively copying expert expertise into automated algorithms that provide consistent, accurate diagnostics without requiring physical presence or manual knowledge transfer between locations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach minimizes downtime and manual effort by providing early fault indication and automatic correction, facilitating the capture and distribution of expert knowledge for accurate and efficient fault detection across multiple facilities.
Implementation Method 1
an audio sensor can be used to detect faults occurring in the equipment
Data Source
AI summary
Systems, methods, and devices for monitoring industrial equipment using audio are described herein. One system includes two computing devices. The first computing device can receive, from an audio sensor, audio sensed during operation of industrial equipment, extract a plurality of features from the audio, determine whether any portion of the audio is anomalous, and send, upon determining a portion of the audio is anomalous, the anomalous portion of the audio to the second, remotely located, computing device. The second computing device can provide the anomalous portion of the audio to a user to determine whether the anomalous portion of the audio corresponds to a fault occurring in the equipment, and receive, from the user upon determining the anomalous portion of the audio corresponds to a fault occurring in the equipment, input indicating the anomalous portion of the audio corresponds to the fault to learn fault patterns in the equipment.