Condition Monitoring Services-Why is Predictive Maintenance Important?
Condition Monitoring Services-Artificial intelligence can help oil and gas companies predict when their machines and equipment need maintenance. Oil and gas companies can repair these machines before their breakdowns result in lengthy downtime or employee injuries that can cost millions in legal fees and damages. All companies in this report claim to help oil and gas, energy, and utility companies with at least one of the following:
• Monitoring the condition of machine resources
• Predict the likelihood of future machine failures
• Make proactive maintenance decisions
• And, consequently, reduced operating costs resulting from catastrophic machine failures We began analyzing how energy companies can use artificial intelligence to predict when their machines will fail with condition monitoring.
Condition Monitoring in UAE
Technomax offers its condition monitoring services, which claim to help oil and gas companies monitor their machine resources, predict future machine failures, and make proactive maintenance decisions using machine learning. Technomax states that various digital technologies and artificial intelligence drive the condition monitoring services.
The company says the machine learning model behind the software has been trained on more than 800 types of assets used in the energy, chemical, manufacturing, and mining industries, millions of components, and the thousand ways they can fail. The application can be applied on edge and in the cloud. The company says the client company's oil and gas experts should determine where to install the sensors on the cylinder. These sensors would then collect telemetry data from those parts of the cylinder, such as pressure. This data would then be used as a reference for a properly functioning cylinder.
Machine Learning in Predictive Maintenance
The machine learning model behind condition monitoring services would have to be trained on millions of these telemetry data points, and data on when certain parts of the cylinder require maintenance and how long it took to service those parts, and possibly how long take time Spare parts to get to the site. The data would then be executed using the software's machine learning algorithm.
This would train the algorithm to discern which of all these data points is related to parts of the cylinder that are in good working order, when the cylinder has needed maintenance in past, which of its features required repair. The software then predict when certain parts of the cylinder need care before breaking.
Technomax advised a team of engineers and asset management from the client, providing condition monitoring services in the United Arab Emirates. After a physical inspection of the tower, the resource management team discovered the problem as expected by the application. This early diagnosis allowed the team to repair the wind turbine at $ 5,000 and reduce downtime. This customer also reports that within about three months of using the Technomax software, the customer has generated high-value information from 10% of their turbines.
We offer condition monitoring services as a preventative maintenance application that helps in various sectors like oil and gas, aerospace, defense, financial services, healthcare, manufacturing, prioritizing equipment maintenance, maximizing uptime, improving worker safety, and reducing expenses using predictive analytics.
By implementing condition monitoring services in the UAE, we can integrate our software into enterprise databases. The model behind our software has been trained on historical fault data.
The user can then upload sensor data, supervisory control and data acquisition (SCADA) data, legacy data, and data such as technical notes and external data sources such as weather that are not tagged in the software.
The algorithm underlying our software system used in condition monitoring services would then be able to detect anomalies in assets and calculate the risk of failure score for each asset by analyzing the asset's operating conditions and performance data. The system estimates the probability of failure over different periods, such as 14 days, 30 days, or six months.