Imagine a scenario in a bottle manufacturing unit. A bottle conveyor machine is working steadily in the bottling plant for a long time. As per the maintenance data, this asset was reliable and high functioning. However, suddenly, its vibrations showed an alarming increase resulting in panic and a shutdown of operations. This scenario could have been avoided if the glitch would have been detected earlier.
How can we detect such errors resulting in the loss of time and resources? By making use of the real-time machine data.
This post will list the steps which can help you in making the most of the real-time machine data with emphasis on the following points:
Detecting performance glitches at the right time
The real-time data generated by machines can help maintenance and operations managers to truly interpret machine health. This data presents wide opportunities to not only detect and monitor machine conditions but also, improve the quality of production and operations.
The vital secret of making the most of machine data is spotting the opportunity of when to take action. Getting the right alert at the right time can be a catalyst in either missing or meeting your production targets.
Actions such as ordering required spare parts, planning a repair activity can be performed without halting the production.
Determining the optimum response time
To establish the right time for taking actions, machines historical performance data should be collected on a regular frequency. Predictive maintenance applications like Eugenie can analyze Real-time data streams to spot expected defects and faults before they occur.
Alternatively, performance deterioration occurs slowly over a period – sometimes weeks, months or years to get a clear picture in terms of data. Such defects can be learned only by a robust trend detection solution ability. Machine learning-based solutions can help immensely in machine health diagnostics through accurate trend detection.
Establishing the asset lifecycle
A comprehensive review of an asset’s data collected over a period enables machine experts to outline its maintenance and operations decisions for the present as well as the future. This review can help them in planning accurately if machines need specific parts or maintenance procedures. Decisions on inventory and planned downtimes can be made better by this information. Decisive factors such as high production value or capital investments can be taken into account to optimize the production.
Implementing actionable Insights
Unexpected downtimes can be catastrophic. To maximize the value of real-time asset data, turnaround time and decisive actions are critical – especially when prompt actions are required.
Recurring defects are easier to detect, however fast and unexpected defects require continuous monitoring, empowered by fast and technically capable algorithms. Accuracy becomes an essential factor where even a five-minute advance notice can prevent a shutdown. Similarly, unnecessary preventive maintenance actions, resulting in a loss of costs and time can also be as damaging.
The ideal turnaround time for maintaining machine health should depend on the extent of criticality and the nature of expected malfunction.
Eugenie is a tailor-made solution to determine accurate machine diagnostics that can help in preventing unexpected machine downtimes. Are you ready to improve your machine reliability?
Get a glimpse of the solution to learn how real-time data can empower machine-related decisions.