Technology
.svg){kind=logo}
As we all know “Anything that can go wrong will go wrong”, any unknown factor could cause unpredictable loss. Especially in the manufacturing industry, which require strictly temperature control in the warehouse, if something goes wrong, it will pay a heavy price.
However, it seems that it is difficult to eliminate unexpected risk if only most basic traditional maintenance management mode is followed, judging from the reliability of the equipment and the effectiveness of the technical support of the maintenance team.
In light of this, AWS launched an industrial machine learning (ML) solution called "Amazon Monitron" few months ago, which claims to help users monitor the status of their production equipment, so as to intervene in timely maintenance before equipment failures to keep the production run steadily.
Roughly speaking, enterprises usually adopt four maintenance modes for production equipment on industrial sites or temperature control equipment in storage environments. The first and the easiest way, is to “do not maintain in advance”, let the production line equipment continue to operate day and night, until the failure is interrupted, and then carry out reliability maintenance, and resume the line after the maintenance is completed. Obviously, the unknown risk to the device in this condition is quite high.
The second is routine maintenance mode. No matter whether there is an immediate need, a routine plan shall be adopted, which means maintenance shall be executed on a regular basis. The advantage of this model is that it can arrange the scope of work beforehand, and it is easier to control the quality of the execution plan, but it is relatively easy to cause excessive equipment maintenance or insufficient maintenance quality, resulting in unnecessary expenses, and even the situation of equipment failure without warning.
As for the third one, it can be called the state-based maintenance mode. For equipment parameters, thresholds, etc., the warning conditions are pre-defined and monitored from time to time. Once the warning value is really triggered, maintenance is performed. The greatest benefit is that it can reduce expenses, and the risk control effect is better than the previous two methods.
Mainly through real-time monitoring on key components of equipment in the actual field, and continuous tracking to discovery of potential failure risks; because further maintenance actions will fall within a predictable time interval, and it is easier to implement more accurate disposal measures, so it is called It is an ideal model that can best take into account the multiple synergies of time, cost, and risk control.
Based on machine learning, Amazon Monitron has the ability to perform end-to-end monitoring, which can help users implement the above-mentioned predictive maintenance model, detect potential signs of failure in equipment in advance, and allow users to carry out predictive maintenance actions in a leisurely manner, effectively reducing the risk of line losses due to unanticipated downtime.
In-depth study of the supply content of Amazon Monitron, including dedicated sensor collectors and gateways for obtaining equipment vibration frequency data and temperature data. Once these devices collect data, the data will be uploaded under the premise of high security. Store in AWS Region and use machine learning technology for analysis; at the same time, managers can use Amazon Monitron's exclusive App to track data status, and can further receive equipment exception notifications, to take timely management measures to make equipment as soon as possible return to normal. (Example: Figure 1)
In other words, in the industrial field, users can quickly establish a highly reliable control mechanism through Amazon Monitron, and then combine with Amazon Monitron's dedicated sensors to continuously collect and monitor the health status information of industrial equipment, such as bearings, vibration and temperature data of rotating machinery such as fans, gearboxes or compressors, the entire operation process does not require any third-party software and hardware. (Example: Figure 2)
It is worth mentioning that MetaAge Technology has considerable experience in how to deploy and install Amazon Monitron in application fields. Through the rapid establishment of wireless connection, action monitoring management, early warning notification and other mechanisms, it can help enterprises to comprehensively solve past encounters, variables such as space constraints, time delays, etc., to achieve predictive maintenance goals more efficiently. Now, MetaAge is the first to share the situation map of the deployment process, so that enterprises can get the first-hand experience of the Amazon Monitron application. (Example: Figure 3)
▲ Figure 3
