In an ideal world, machines would be 100 per cent reliable in performing their intended functions with no adverse effects. Unfortunately, such a world does not exist. Well, at least not for now.
Machines today play an integral part in modern industrial processes. And while the use of machinery has accelerated operational processes, simplified tasks for humans, and reduced the risks involved in manual labour, machine failures are an unavoidable reality, and the price of that is costly.
The rise in workplace injuries in the industrial sector
Over the years, we have been facing a significant increase in workplace injuries, and machinery fault is one of the key contributing factors. According to the National Statistics of Workplace Safety and Health Report in 2020, one of the top two causes of major workplace injuries was Machinery Incidents.
Severe injuries, such as amputation accidents, can impact the workers’ lives and livelihood. In worst-case scenarios, it may even result in the death of workers. This is why measures must be put in place for a safe working environment.
As leaders, we strive to establish a safe environment for our employees. It is also a company’s moral and legal obligation to provide a safe and healthy workplace for all its workers by ensuring that all grounds are covered for workplace safety.
The rise of workplace injuries resulting from malfunctioning machinery thus compels industry leaders to take up measures that help in the early prediction and detection of machinery faults.
A sound-first predictive maintenance solution
By using an AI-based predictive maintenance solution, companies can monitor their machines in real-time and be alerted about potential breakdowns or other malfunctioning issues so that necessary actions can be taken to neutralise the threat before any catastrophic incidents happen.
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This allows managers to better protect their workers from needless injuries and fatalities. AI should also be used to empower and protect workers by seamlessly integrating the technology into existing legacy systems in factories or operational sites, mitigating risks across all aspects of the industry and enhancing the intelligence and safety of our workers.
When there are underlying problems in a machine, it frequently produces a different sound, even before these problems escalate into something more severe. A sound-first predictive maintenance system would thus be key to early detection and condition monitoring.
Such sound-based approaches typically consist of two steps:
- Condition-based Monitoring provides real-time equipment diagnostics through sound detection, analogous to an “Apple Watch” for machines.
- Predictive maintenance acts as a “crystal ball” to help predict equipment breakdowns in advancSound sensors can easily pick up the differences in sound given offers. The system will flag them as anomalies for further action.
Round the clock surveillance
On top of that, AI predictive maintenance systems can work around the clock to provide real-time alerts and discover anomalies 24/7 to ensure that the workers can safely carry on with their work throughout the day.
Established AI systems even offer pre-existing data reservoirs that the AI can utilise as a reference to detect anomalies without requiring companies to start new training models from scratch.
Such databases help springboard companies by giving them a headstart in deploying the solution and identifying machine faults with minimal calibration time, making retrofitting easier.
What’s more, over time, as more data is collected, the self-learning AI will learn to recognise the patterns of sound anomalies and make even more accurate predictions.
In some cases, simplified colour-coded alerts are also put in place to help less-skilled workers identify potential issues with machinery with ease and preemptively address them before they worsen, which is an effective use of resources and time.
This can also free up their capacity and time for upskilling, allowing them to take on value-adding responsibilities, become more versatile and diverse in their skillsets, which is critical in today’s economy.
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By tackling the right problem at the right time, organisations can also save on material costs from machine replacements and reduce their ecological footprint by minimising wastages incurred from redundant machine parts replacements. Essentially, such solutions save money, save time, and save lives.
AI is the new inevitable
Just last year, the Singapore Government invested US$180 million in AI research and expanded funds to stimulate the use of AI technology across industries. As AI solutions become more efficient and effective, it is critical for asset-intensive industries to implement predictive maintenance systems to improve their overall efficiency, reduce downtime and enhance the safety of the workers.
According to a 2019 report by Allied Market Research, the global predictive maintenance market was initially estimated at US$4.3 million in 2019 and is now expected to expand more than sevenfold to US$31.9 million by 2027.
Predictive maintenance is the cornerstone of a safe industrial environment. But it is with near certainty that with the help of machine learning technology, sound-based predictive maintenance solutions will become a vital tool across industries, like an OS layer for all industrial machinery, similar to what Microsoft achieved for PCs.
We live in a society where machines are constantly functioning to fulfil the world’s ever-increasing needs. And with workplace safety becoming a rising concern, industries will need to ensure the fulfilment of these needs without compromising the safety of their workers.
By adopting sound-first predictive maintenance, industries such as maritime, construction, manufacturing, and oil and gas can do that.
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