Superiority of acoustic emissions based predictive maintenance

It is being acknowledged by many industrial manufacturing heavyweights that Neuron Soundware really excels in developing AI and ML algorithms to optimize industrial manufacturing equipment and processes through sound and other physical parameters analysis.

Acoustic monitoring is a type of predictive maintenance technology that uses ultrasonic and acoustic imaging to detect sound waves at frequencies that are inaudible to humans. The advantages of acoustic monitoring include early detection of potential faults, real-time knowledge of asset health, and the ability to maximize asset lifecycles. Acoustic monitoring is non-invasive, versatile, and cost-effective, and can be applied to a wide range of machines and systems. It can be used in various industries and domains, such as manufacturing, energy, transportation, and healthcare. The use of sensors and handheld ultrasound tools paired with software can be crucial parts of a predictive maintenance program. The overall benefits of condition-based monitoring include increased uptime, reduced downtime, decreased maintenance costs, increased asset life, and greater ease in prioritization and planning of work orders.

Neuron Soundware’s SVP Marketing & Sales Executive, Pavel Trojánek, described the superiority of this method: “While vibration analysis is still being used, acoustic analysis has gained popularity among technicians for its superior prediction of imminent breakdowns and its ability to capture and interpret ultrasonic signals, which can lead to optimized asset performance and the prevention of costly breakdowns. Additionally, it has been observed to perform better than vibration analysis in predicting failures of equipment. Acoustic monitoring-based analysis is considered more accurate than vibro analysis for predictive maintenance due to its ability to detect certain faults earlier and its cost-effectiveness. Acoustic monitoring systems can imitate the hearing abilities of experienced workers to diagnose malfunctions by sound, making it a valuable tool for enhancing maintenance strategies.”

Perhaps the debate whether AI and machine learning based predictive maintenance utilizing machines’ acoustic emissions is more accurate than the predictive maintenance utilizing the traditional vibro analysis can be finally put to bed.