Predictive maintenance is a technique that helps monitor the equipment condition as well as to determine the appropriate maintenance activities. This is done through artificial intelligence (AI), machine learning and predictive analytics. The system will be able to predict the future trend of the equipment’s condition i.e. potential failure and prompt the users to perform maintenance before the machine fails.
Therefore, through predictive maintenance, businesses will be able to achieve the most efficient and cost-effective way to optimize their equipment performance and maintenance works.
How does predictive maintenance work?
We deploy condition monitoring sensors to gather data on machine health and we upload these collected data onto a centralized database for analysis. As such, the system can observe the patterns and trends of machine performance. With these insights, the system can then accurately predict the likelihood of machinery malfunctioning and trigger notifications to operators to arrange for the necessary maintenance works.
Various condition monitoring sensors are commonly used in identifying and monitoring machine health, e.g. vibration, thermal, current, sound, etc.
Vibration sensors detect an overall increase in machine vibration, which would indicate a potential machine issue.
Thermal sensors are used to monitor the crucial machine parts as the elevated temperature is an indication of potential issues.
Current sensors monitor the current draw of a machine’s motor. An increased current draw could indicate potential issues with the motor.
Sound sensors detect the noise generated by the machine and anomalies identified is an indication of potential issues.
At groundup.ai, we are laser-focused on the use of sound in monitoring your machine’s health.
Why we believe in the use of sound
Along with the growth and advancement of AI, sound-based predictive maintenance has also made a breakthrough. The technology uses sound anomalies to detect potential problems in machines. This is because the movement of components in the machine creates friction and noise. Moreover, each equipment has its own “sound fingerprint” and the system can recognise the individual’s sound profile.
Early identification of faults and problems are crucial when it comes to machinery. The system can detect malfunction components at an early stage by monitoring the sound anomalies before they pose a big problem. Hence, we are using sound as a leading indicator of machine fault as compared to other measurements. This is also the reason why we strongly believe that sound is the most evident indication of machine failure.
For instance, minor problems such as misalignment, looseness, loss of lubrication and imbalance often go unnoticed until they snowball into a bigger problem such as overheating. An elevated temperature could indicate potential problems, but it often takes a while for the temperature to hit a certain threshold before red flags are triggered. This is more of a lagging indicator.
Meanwhile, the sound is a leading indicator as the sensors are extremely sensitive. They can pick up the noises created by the problems easily and the system will show them as anomalies. Therefore, a sound-based predictive maintenance system can accurately determine the machine condition through listening.
Furthermore, most experienced technicians and engineers also rely on the noises made by the machine to identify potential issues. However, it is subjected to human biases and sometimes they may not be able to hear everything. Besides, humans are also a limited resource and are not able to monitor the machine 24/7.
Therefore, we create sound-based predictive maintenance to “listen” and monitor your machine at all times. The system will detect any abnormal sound and inform the operators immediately and actions can be taken quickly too.
Another benefit of sound-based predictive maintenance is that it is a non-intrusive monitoring approach. There is no physical contact between the machine and the sensors. We only need to deploy the sound sensors in close proximity to the machines and not attach them to the machines, this provides a great deal of versatility and flexibility in deployment.
How does sound-based predictive maintenance work
Data collection: We will set up the sensors close to the machines to capture all the sound made by them.
Data exploration: Along with the help of your technicians, we will use machine learning algorithms to identify and label the sample sounds. This is to help the system to know the various sound profiles produced by the machines. For examples, when the machine is operating, idling, in standby or when it requires maintenance. Over time, the system will become smarter and less human input is needed.
Algorithm calibration: Machine learning algorithms will be able to differentiate the sound made by your machine. Imagine if we are in an incredibly noisy engine room, it would be hard to detect whether the sound is coming from a poor performing machine. However, we can remove the redundant ambient noise with our proprietary method, enabling us to analyze the sound accurately and focus on the root issue. The system will pick up the abnormal sound and alert the operator where maintenance works can be promptly arranged.
What are you waiting for?
Sound-based predictive maintenance is the future of predictive maintenance. Sound is not just a leading indicator, it is ambient as it is not limited by angles. Moreover, it applies to the majority of the machine components.
We are in a world where machines are constantly under pressure to be performing all the times, where lagging indicator are no longer sufficient. Therefore, businesses need to receive signals of potential failure instantaneously to take appropriate interventions. This is why you should adopt sound-based predictive maintenance, and we believe this is the most efficient and cost-effective approach to predictive maintenance.
Here at groundup.ai, we have extensive expertise in the field of AI and predictive analytics. Our technology is highly capable of supporting you with the implementation of sound-based predictive maintenance for your equipment. Have a chat with us to find out more.