Acoustic artificial intelligence (AI) solution provider DIPLI has supplied its acoustic AI solution to a global large port operator. Following its applications in semiconductor, railway, and shipbuilding facilities, the company is expanding its reach into the industrial predictive maintenance market.
DIPLI announced on November 4 that it has provided its industrial acoustic AI solution, 'Listen AI,' to a global large port operator managing facilities in Busan, performing acoustic-based predictive maintenance for automated crane equipment.
Listen AI analyzes sounds generated in industrial settings to diagnose the quality of components and the condition of equipment. It is utilized for quality inspections of industrial parts, as well as for checking the sounds of connectors and bolts, and for predictive maintenance of equipment. The technology can extract target sounds and distinguish between normal and abnormal sounds even in environments with noise levels exceeding 80 decibels (dB), making it applicable across various industries including mobility, home appliances, ports, and robotics.
This solution has been implemented at an unmanned automated yard crane (ARMGC) at one of the terminals in Busan New Port operated by the global port operator. Acoustic sensors have been installed on two motors and a gearbox in the crane's hoisting mechanism to collect sound data 24/7 and monitor the equipment's condition.
Specifically, the system synchronizes the collected sounds with control signals related to the crane's ascent and descent, speed, load, and position, establishing a baseline for normal operating sounds. Based on this, it distinguishes between normal and abnormal sounds, sending alerts to the terminal's crane monitoring system (CMS) and remote offices when anomalies are detected for inspection and action.
Unlike vibration sensors, acoustic-based predictive maintenance can be installed in a non-contact manner, allowing for application without modifying the equipment. This enables continuous monitoring of automated equipment that is difficult for operators to access. The analysis server is set up on-premises within the machine room, and remote monitoring is conducted through a secured wireless router.
Lee Su-ji, CEO of DIPLI, stated, “Thanks to the advantages of non-contact installation and the ability to detect anomalies through sound before vibrations occur, we have been able to apply our acoustic-based predictive maintenance solution to the port sector, expanding beyond semiconductor, railway, and shipbuilding facilities. We will continue to develop our solutions to ensure high accuracy and expand our supply pipeline.”
* This article has been translated by AI.
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