TECH OFFER

Sound-based Artificial Intelligence (AI) Solution for Predictive Maintenance of Machinery

KEY INFORMATION

TECHNOLOGY CATEGORY:
Electronics - Sensors & Instrumentation
Infocomm - Speech/Audio Processing
TECHNOLOGY READINESS LEVEL (TRL):
LOCATION:
Czech Republic
ID NUMBER:
TO095021

TECHNOLOGY OVERVIEW

This technology offer is a predictive diagnostic technology based on sound, for the early detection of machines which are going to break down. By analysing the sounds produced by machines in their routine operations, artificial intelligence (AI) algorithms are able to determine if these machines are functioning properly, or if they are on the verge of breaking down.

TECHNOLOGY FEATURES & SPECIFICATIONS

In this smart diagnostic system, acoustics sensors collect sound data from machines and equipment in the normal course of their operations.

The data is then processed by AI algorithms. Probabilistic methods are used to predict the risk of an outage of each individual machine. The integrated hardware and software platform automatically gather data in real time, and continuously assess the equipment health.

An innovative digitization and analytics platform enables end users to monitor their critical machinery in real time, at a distance. Hence, anomalies can be flagged out at an early stage.

POTENTIAL APPLICATIONS

The potential applications of this Technology Offer are - real time health monitoring and predictive maintenance of machines and equipment such as engines, compressors, robots, etc.

Market Trends & Opportunities

According to Roland Berger, the global market for predictive maintenance is expected to grow at a CAGR of 27% from 2016, to USD6.3B in 2022. 

Benefits

The lack of predictive maintenance increases the risk of costly unplanned downtime. With this sound analysis technology, the user is offered a simple and effective method of predictive malfunction diagnostics, thereby avoiding costs incurred due to unforeseen downtime or quality issues.

Complete machine failure can also be avoided from this early warning system.

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