As the saying goes, a stitch in time saves nine. Solving problems before they occur is the best way to prevent potential disasters. This is where predictive maintenance comes into play. It anticipates equipment malfunctions based on data-driven analysis of signals collected from machinery through sensors during operation, via various means, such as acoustic monitoring. This allows for early detection of anomalies, proactively identifying, or even predicting, issues so that they can be prevented or addressed at an initial stage. Predictive maintenance can not only reduce downtime, but also optimise maintenance schedules, maximises reliability, and prolongs equipment life. (Source 1) Incorporating acoustic monitoring for predictive maintenance could help detect hidden machinery issues beyond what can be identified visually, while utilising data algorithm and artificial intelligence (AI) can enhance the accuracy and efficiency of defect detection and analysis. This article will present some AI predictive maintenance examples.
Acoustic AI: Detecting and Predicting with Sound Data
Among the various types of predictive maintenance technologies, acoustic monitoring for predictive maintenance relies on monitoring, capturing and analysing sound data (Source 2) through acoustic sensors. Through analysing the sound data, it identifies operational patterns and detects equipment anomalies that may signal potential defects. How can we take the effectiveness of acoustic detection for predictive maintenance to a new level? Machine learning and artificial intelligence (AI) could be the answer.
As well as reshaping our everyday work and life, AI also holds the potential to greatly enhance the capability of predictive maintenance by leveraging algorithms and data analysis to better anticipate equipment defects (Source 3).
Acoustic AI is a cutting-edge innovation combining acoustic monitoring, predictive maintenance, machine learning, intelligent audio signal processing, and advanced acoustic sensor technology. It improves the accuracy of acoustic detection and analysis through algorithms and data-driven analytics. The algorithms will help identify equipment issues through sound data matching, analysis and acoustic diagnosis, turning the data into actionable insights for more accurate, efficient and proactive predictive maintenance.
Benefits of Acoustic AI for Predictive Maintenance
Implementing acoustic AI for predictive maintenance offers numerous benefits:
- Reduced downtime – by spotting anomalies and malfunctions early, acoustic AI technology can minimise service interruptions and downtime, resulting in higher productivity.
- Improved sustainability – proactively maintaining equipment with acoustic AI enabled predictive maintenance reduces downtime also means reduced energy consumption and carbon emission related to additional repairs.
- Enhanced safety – acoustic monitoring for predictive maintenance with sensors can replace manual inspection procedures, especially in complex, dangerous and risky industrial environments, for instance power equipment maintenance where technicians used to climb up high electric towers for inspection.
- Better accuracy– data-driven analysis can enhance the accuracy of mechanical malfunction investigation and detection.
- On-going improvement –AI has the capability to continuously learn, adapt and evolve, enabling on-going enhancement of the analysis.
- Increased cost-effectiveness – by reducing breakdowns, ensuring structural integrity, increasing equipment reliability and extending equipment lifespan, acoustic AI improves the overall cost-effectiveness for businesses.
Huge Potentials of Acoustic AI
With its capability for a broad range of applications, acoustic AI is unlocking new opportunities and opening doors to new possibilities, AI predictive maintenance examples such as machinery fault detection in different industries, quality inspection, underground water pipe condition monitoring, as well as extending to fields such as healthcare and agriculture. (Source 6)
Acoustic detection solutions go beyond monitoring mechanical degradation and maintaining machine health—they are also useful for monitoring human health conditions, especially in detecting diseases and health issues. [1] One of the AI predictive maintenance examples is illustrated in the following, by collecting acoustic data like sounds of snoring and teeth grinding, AI algorithms can be leveraged for analysing sleep patterns to identify sleep disorders. Moreover, cough patterns can be monitored for detecting and tracking respiratory disorders. (Source 2)
Among the various applications of acoustic AI, machinery and equipment monitoring stand out as a sector with significant growth potential. The global predictive maintenance market is expected to expand from approximately US$7.5 billion in 2023 to over US$66 billion by 2031, at a CAGR of 31%. (Source 4) In Mainland China, acoustic AI is gaining traction across industries , including manufacturing, energy and power, petrochemical, and railway transportation, etc. (Source 5). Companies like Ensonic, a leading provider of acoustic AI solutions based in Mainland China, exemplifies the innovative momentum driving this transformation.
Ensonic – the Pioneer of Acoustic AI in China
Established in Mainland China in 2017, Ensonic is an industrial acoustic technology provider pioneering in acoustic detection and analysis for a diverse range of industries such as manufacturing, energy and renewable energy, power, smart city and railway transportation applications. (Source 7)
Riding on its proprietary microphone arrays for acoustic monitoring and sound data collection, as well as advanced signal processing technology, the company has become a trailblazer in acoustic AI-based predictive maintenance solutions. Its solutions are now adopted across power grid transformers, lifts and escalators, train components, hydropower plants, wind power equipment and data centres.
MTR Lab recently announced its strategic investment in Ensonic, marking its first investment in Mainland China. This move aligns seamlessly with MTR Lab’s mission to advance smart city development and promote sustainability through innovation and technology investments. Additionally, it accelerates the transfer of cutting-edge technologies from Mainland China to the world. (Source 8)
MTR Lab’s funding will support ongoing technology research and development of Ensonic, while empowering the company to go the extra mile – on the one hand further developing solutions to meet international standards, and on the other hand facilitating its overseas expansion. This also represents a major step of MTR Lab in cultivating home-grown acoustic AI solutions, while echoing the national strategy of developing “new quality productive forces” (新質生產力).
Want to learn more about MTR Lab’s role as an international investor and a collaborative platform connecting like-minded partners, fostering global connections and advancing innovation to co-create a smarter, greener future? Please visit: www.mtrlab.com.hk/en/investment-portfolios/
Source 1: Integrating Servopress Acoustic Monitoring with Machine Learning for Enhanced Predictive Maintenance
Source 2: How the Sound of AI Benefits Our Lives
Source 3: How AI Is Used in Predictive Maintenance
Source 4: Predictive Maintenance Market Insights
Source 5: Listen Up! Acoustic AI Hears a Smarter and Safer Future!
Source 6: A Survey on Artificial Intelligence-Based Acoustic Source Identification22024-0~1.PDF024-0~1.PDF
Source 7: Ensonic Technology Limited
