Investing in new technologies is the best way to guarantee the reliability and availability of assets. Modernizing the industrial process is essential to streamline tasks and reach productive excellence.
The market for condition monitoring sensors is constantly growing and evolving. This is mainly due to their effectiveness in preventing unexpected breakdowns, as well as their invaluable assistance in planning maintenance routines.
A manual vibration collector is very commonly used in the industry. Its main function is to measure the general vibration for acceleration, speed and distance in a wide range of machines.
For manual collection, measurements must be taken physically next to the asset and are shown in an IHM display. Afterward, these results are stored in an internal memory with a given measurement capacity and can later be downloaded to a computer.
This method works well in teams that are willing to collect data on-site. However, manual vibration collections are more costly – as they require more labor – and less immediate.
IoT sensors don’t depend on on-site data collection. From the moment the sensor is positioned on the equipment, a technician does not need to worry about gathering new data or physically visiting the asset. The process is automatic and does not depend on external interferences since it is completed 100% remotely.
To ensure your company is effectively equipped, we need to learn more about the equipment and platforms needed in order to obtain complete condition monitoring.
Besides the Smart Trac sensor, the TRACTIAN solution consists of a maintenance management platform available for unlimited users. There’s no need for special training and the information can be consulted and managed by maintenance specialists 24/7.
Currently, IoT is the most advanced method for monitoring and collecting data from a machine. This uses a mobile network to transmit data (3G/4G) which allows for total independence, i.e., it does not require special IT infrastructure to be utilized.
TRACTIAN technology uses data analysis, machine learning and statistics to enable a thorough evaluation of asset health and behavior.
We provide something similar to a medical diagnostic for a machine where we describe the characteristics identified during a period of time. An analysis is made with the corresponding problem related to these characteristics. If an asset is not in normal operation, the first symptom will be abnormal vibrations.
The data acquired can be divided into two types:
The vibration and temperature data are acquired the moment the sensor is placed on a machine. This data provides information about the mechanical behavior and physical condition of the asset.
As for the asset data, this is an overall term used to describe specific data about the machine including the asset model, RPM, power, limit temperature, limit downtime, bearings data, gears, fans, belts and pulleys.
Once uploaded, this data has to be processed. Predictive maintenance platforms are excellent tools as they contain thorough data processing models.
When this data is combined, it provides information that will serve as a base for the metrics formation and will later help in the diagnostics of an asset’s health.
All this data will be transformed into clear and relevant information. This process is necessary for the storage and massive data analysis, known as Big Data.
After mapping and selecting the assets to be monitored, all you have to do is place the sensor in the right position in each machine.
Since it relies on plug & play technology, the IoT sensor is extremely practical and can be installed in less than 3 minutes.
TRACTIAN sensors were developed to meet the needs of maintenance teams in the best way possible. In many cases, cable or battery versions are only able to precisely measure 2 things:
TRACTIAN sensors automatically detect:
We can monitor more than 30 types of assets that work in some kind of critical behavior or defined vibration profile.
Among them, we can highlight four of the most common assets:
Unlike the traditional method, our solution is focused on the day-to-day needs of maintenance teams to make decisions efficiently.
AmstedMaxion, the biggest manufacturer of steel wheels and casting components in Latin America, and TRACTIAN first got in touch in November 2020. Right after that, in January 2021, 12 sensors were installed in their most critical assets.
Before the use of sensors, unexpected breakdowns were almost common in AmstedMaxion as their maintenance supervisor, Tiago Junqueira, stated :
‘We had a significant increase in the amount of information about the assets with the use of sensors and the platform itself. We managed to transform all of that knowledge, to understand the occurrences and events preceding and coming after, so we prevented several breakdowns.’
On top of that, there was a 40% decrease in equipment vibration and a savings of around $50,000 in repair costs.
AmstedMaxion’s example shows the efficiency of IoT sensors in predictive maintenance. When put into practice, sensors lower the need for costly repairs and enhance the reliability of machines, saving time and profit.
Would you like your company to be like AmstedMaxion and adopt the market’s most comprehensive maintenance system for predictive maintenance? Contact TRACTIAN and schedule a demo.
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