Condition-based Monitoring and Predictive Maintenance
Condition-based Monitoring or Condition-based Maintenance (CBM) is a maintenance technique that uses sensors to monitor the status of equipment in real-time during operation. The data collected from the sensors will aid in determining whether and when maintenance should be performed. It can be seen as essential to Predictive Maintenance (PdM) strategy. This is because the collected data can be used to derive useful insights. Thus, allowing businesses to identify potential machine failures and project the equipment’s remaining useful life (RUL).
Why is it important to monitor your machine’s health?
One of the most crucial benefits of condition-based monitoring is that it reduces unplanned downtime, which is one of the major issues that impede productivity. Not only does it hinder productivity, but it also brings about operational nightmares and additional costs to handle the sudden catastrophe. Unplanned downtime is a major problem that transcends the industrial sector. Because most, if not all, depend on heavy machinery for their day-to-day operations. Statistics have also shown that unplanned downtime in the manufacturing industry leads to a loss of customer loyalty and productivity.
Hence, businesses should look into deploying Condition-based Monitoring to prevent unplanned downtime. And in turn, to achieve the most efficient and cost-effective manner to optimize their operations.
How does Condition-based Monitoring work?
Monitoring sensors are deployed to gather data on a machine’s health and performance. Afterwhich, the collected data is uploaded onto a centralized database for analysis. The AI system identifies the patterns and trends of the machine’s performance through analyzing the data. The real-time data captured and analysis insights will be shown on a dashboard for easy reference. In the case that an anomaly is detected, the system will trigger notifications to operators. So that they can arrange for the necessary maintenance works before any serious breakdowns occur.
Common types of condition monitoring sensors
The common types of condition monitoring sensors used in monitoring machine health include vibration, thermal and voltage sensors.
Vibration sensors are used to detect an overall increase in machine vibration, which could indicate a machine failure.
Thermal sensors are used to monitor the real-time elevated temperature of the machine, allowing early detection of wear and tear as well as poor machine conditions.
Voltage sensors are used to identify machine degradation due to sudden surges in machine voltage electrical emission.
Such sensors are commonly used in industrial machinery and most organizations already have them in place to help keep track of the relevant metrics. These common indicators, however, have inherent limitations that may restrict the system’s capability to provide a more holistic and comprehensive examination of a machine’s health.
As such, businesses can adopt condition-based monitoring using sound sensors and sound analysis to obtain a better understanding of their machines. And at Groundup.ai, we are laser-focused on the use of sound in monitoring your machine’s health.
Why do we believe in the use of sound?
Along with the growth and advancement of AI, sound-based Condition-based Monitoring has made a breakthrough. The technology picks out anomalies from the sound generated by the machine to detect potential issues. The anomalies are often detected by sound monitoring tools before vibration or temperature monitoring tools can detect them.
Moreover, each piece of equipment has its own “sound fingerprint” and the AI system can recognise the individual’s sound profile. Good quality sensors are now capable of picking up subtle sounds from machines. Even if the frequency is outside of a regular human’s hearing range.
Early identification of faults and problems are crucial when it comes to machinery. The system can detect malfunctioning components at an early stage by monitoring the sound anomalies before they pose a big problem. Hence, the sound is the most evident indication of machine failure as compared to other monitoring tools.
Why is using sound a better option?
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. Hence, this is more of a lagging indicator.
Meanwhile, the sound is a leading indicator as machines often give off a different sound when there are underlying problems, even before these problems escalate into something catastrophic. The differences in sound can be easily picked up by the sound sensors and the system will flag them as anomalies. Therefore, sound-based condition-based monitoring can accurately determine the machine condition through listening, and at a much earlier stage compared to other types of indicators.
In fact, most technicians and engineers also rely on the sound made by the machines to identify potential issues. However, it is still subjected to human biases, and manpower is a limited resource. Hence, most businesses are not able to arrange for technicians and engineers to be around 24/7 to monitor the machines.
Therefore, sound-based Condition-based Monitoring will be an excellent solution to adopt to reduce the reliance on manpower and to “listen to” and monitor your machines at all times. Should any abnormal sound be detected, the system will inform the operators immediately and actions can be taken quickly.
Another benefit of sound-based Condition-based Monitoring is that it is a non-intrusive monitoring approach as no physical contact is required between the machines and the sensors. The sound sensors will only need to be deployed somewhere near the machines and not attached directly onto the machines. This provides a great deal of versatility and flexibility in deployment.
How does sound-based Predictive Maintenance work
Data collection: The sensors will be installed near the machinery to capture all the sound produced by them.
Data exploration: Through the use of machine learning algorithms, it helps operators to identify and label the sample sounds. The AI system will then identify and capture the various sound profiles produced by the machinery. For instance, when the machine is operating, idling, in standby or when it requires maintenance. Over time, the system will become more intelligent, requiring less human input.
Algorithm calibration: Machine learning algorithms will be able to differentiate the sound made by your machine. Imagine being in an incredibly noisy engine room where it’s difficult to tell if the sound is coming from a malfunctioning machine. The unnecessary ambient noise can be removed using Groundup.ai’s proprietary noise cancellation methodology, allowing us to accurately analyze the sound and focus on the root issue.
With more data being collected over time, the system can even predict when the next machine failure will happen and determine the machine’s Remaining Useful Life (RUL). This helps organizations to make better strategic decisions i.e. whether to fix the machines or replace them altogether, way before any catastrophic breakdowns happen.
Groundup.ai’s Meta Asset Capsule™
You may wonder how do we know if the captured sound data is truly the sound produced by the machines? This is made possible with Groundup.ai’s Meta Asset Capsule™, the world’s most comprehensive sound error database. We help our clients to get a head start in deploying the solution and identifying machine faults with little time needed for calibration. Therefore, companies that work with us do not have to start from ground zero because of our existing database. They also see a faster return on investment (ROI) upon adoption.
What are you waiting for?
We live in a world where machines are constantly running to produce high-quality goods to fulfil the world’s ever-increasing needs. Therefore, businesses must be able to detect potential failures instantaneously to take appropriate interventions and avoid any major operational disruptions and financial losses.
With technological advancements, it is now relatively easy for industrial machine users to get hold of such insights. Condition-based monitoring using sound analysis is one solution that companies should quickly consider adopting to start saving costs, saving time, and creating a safer working environment for their employees. Here at Groundup.ai, we see ourselves as a catalyst to help industrial companies transform into ‘Industry 4.0’ and beyond. Have a chat with us to find out more about our Predictive Maintenance solution using our proprietary IoT sound sensors and AI platform can help your organization move towards Industry 4.0.