Tuesday, January 25, 2022

Researchers propose AI-based approach to contactless machine failure detection

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Prof Rytis Maskeliūnas, Faculty of Informatics, Kaunas University of Technology, KTU. Credit: Kaunas University of Technology

The world’s largest producers lose 1 trillion {dollars} per yr to machine failure. Many issues lie within the noisy manufacturing unit surroundings—working gear and processes produce excessive sound, consequently, equipment faults are sometimes unheard or for that purpose detected too late. Researchers from the Kaunas University of Technology (KTU) have proposed a man-made intelligence-based methodology for various mechanical failures detection in a loud surroundings. The new resolution shouldn’t be solely sustainable—gear will be simply digitalised, with out transforming it—but in addition comparatively low value.

Anomaly detection of business machines is a technique that depends on totally different information—temperature, strain, electrical present, vibration, and —all from sensors put in inside the machine itself. Even although sensors are important in capturing fundamental diagnostics, they’re tough to arrange in older generations of manufacturing unit traces because the equipment could be very “mechanical” and “not digital.”

“For factories with low automatisation levels, many of which remain much larger than autonomous manufacturing lines, failure detection without employing new sensors for each industrial machine is extremely important. As the sound data is easy to collect because of the relatively low installation cost of contactless microphones to existing facilities, sound data-based methods are of great interest,” explains KTU researcher Rytis Maskeliūnas, the co-author of the invention.

However, in extremely noisy manufacturing unit environments, the sound information will get contaminated and interrupted, leading to misinterpretation of the sounds and mistakenly indicated mechanical failures. The group of multimedia and from KTU recommended deep machine studying (ML) methodology that depends on real-life sound information from working industrial and can be utilized for machine diagnostics with no pointless installations of latest sensors. According to Maskeliūnas, failure detection is predicated on coaching algorithms with real-life sound information inside actual industrial equipment sound info.

“The new software solution is cheap and easy to use—the only equipment needed is a microphone pool and a processing device. Artificial intelligence allows acoustic anomaly detection with no additional sensors,” explains Prof Maskeliūnas.

A sustainable resolution to assist digitize the business

“The purpose was to improve the robustness of anomaly detection in the domain of mechanical motion. This is a perspective field, because of sustainability and the opportunity to digitize the industry without getting rid of old equipment as new factory installations require a lot of resources and will not happen any time soon in a lot of poorer countries” says Maskeliūnas.

The experiments have been carried out on the Industrial Machine Inspection and Inspection Malfunction Investigation and Inspection (MIMII) – a sound dataset of business machine sounds. According to Maskeliūnas, this information set includes 4 distinct varieties of equipment: valves, pumps, followers, and slide rails. The waveform audio file (.wav) format was used to retailer the information that comprised machine sound and noise.

“The noise is real manufacturing environment sound that was intentionally blended with pure machine sound at three different SNR—signal-to-noise—levels: 6 dB, 0 dB, and 6 dB. The machine sound was recorded for both normal and abnormal conditions. As a result, we proposed an anomaly detection system for the analysis of real-life industrial machinery failure sounds,” says Maskeliūnas.

Machine failures are time-dependent

According to him, the incorporation of acoustic new sensor applied sciences mixed with deep studying strategies can be utilized to keep away from pointless substitute of apparatus, cut back upkeep prices, enhance work security, improve the supply of apparatus, and keep acceptable ranges of efficiency.

“Early warning can be obtained through the predictive maintenance system based on acoustic failures recognition. The ability to detect weak signals may have a strong strategic impact. Their key benefit is real-time management and planning, which helps to cut down on the costs of production downtime,” says Maskeliūnas.

The group of KTU researchers plans to detect extra varieties of failures: “Like most researchers, we are limited by the amount of data we have. A partnership with a manufacturing company would allow us to gather different scenarios and apply the method more widely. Our solution is particularly relevant in countries with little digitisation where companies do not have resources for new equipment.”

The novel approach in the direction of acoustic anomaly detection has already acquired inquiries for implementation in industrial environments. Maskeliūnas notes that its biggest benefit is low value and no set up required—solely a recording is required.

Sound software program for fault detection in equipment

More info:
Yuki Tagawa et al, Acoustic Anomaly Detection of Mechanical Failures in Noisy Real-Life Factory Environments, Electronics (2021). DOI: 10.3390/electronics10192329

Researchers propose AI-based approach to contactless machine failure detection (2021, November 29)
retrieved 29 November 2021
from https://techxplore.com/news/2021-11-ai-based-approach-contactless-machine-failure.html

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