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Why Predictive Maintenance Analytics Is So Popular?

Published on 28 June 18
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Let’s begin with a simple premise. What does a corporation want? The answer is, of course, the profits? And how those profits are earned? The answer is by lowering the loses and increasing the sales. So, what does one corporation to achieve that? Indeed, it uses predictive maintenance analytics the state-of-the-art technology to minimize the loses by plugging in the loopholes in the production and maintenance wings of the company. Apart from this it also boosts profits by fine-tuning the marketing and consumer satisfaction.

How does real-time data analytics is helping the businesses is no secret? It is analyzing their big data and giving them actionable intelligence by analyzing the data and categorizing it so that actionable insights are available to the companies in real time.

Predictive maintenance analytics has become number one choice of companies because of its efficiency in fault detection during early stages and thus reducing unscheduled downtime. Apart from this, It increases productivity, improves quality and provides the feeling of safety and reliability to staff. In process industries, such as steel, cement, power generation and others even a small downtime can lead to huge loses. A glitch in the production line in a car factory can put its production targets haywire. And this where predictive maintenance in plants is becoming almost a necessity.

What predictive maintenance analytics does is that it makes sense of the data coming from various sensors in the production line. By monitoring utilization of instruments, equipment condition is monitored, and internal component faults are identified, measured, and quantified.

This is vital for keeping the production process free of any malfunctioning components. Through this process, critical failure of mechanical equipment can be avoided while extending the life cycle of monitored equipment.

Moreover, machinery problems occur at specific frequencies, vibration analysis can pinpoint problems without guesswork. By analyzing the vibrations you can get the information you need for accepting new equipment, identifying problems for repair and after overhaul to assure machinery reliability. There are multiple cost benefits that come with predictive maintenance analytics. In this analysis, process sensors are placed on equipment and the data is collected, processed and alerts are generated based on the vibration pattern. As soon as some alert is sounded maintenance is scheduled during non-working hours and the machine is repaired before it fails. Thus, avoiding a complete breakdown and replacing the component even before it gives in.
Predictive maintenance by real-time data analytics allows potential problems to be fixed before failure occurs, which create safer conditions for employees along with increasing the revenue that would have lost due to complete breakdown. Instead of replacement of the entire piece of equipment due to critical failure, a repair can be made prior to failure and cost is minimized to the price of the component and the labor needed for the repair.
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Let’s begin with a simple premise. What does a corporation want? The answer is, of course, the profits? And how those profits are earned? The answer is by lowering the loses and increasing the sales. So, what does one corporation to achieve that? Indeed, it uses predictive maintenance analytics the state-of-the-art technology to minimize the loses by plugging in the loopholes in the production and maintenance wings of the company. Apart from this it also boosts profits by fine-tuning the marketing and consumer satisfaction.

How does real-time data analytics is helping the businesses is no secret? It is analyzing their big data and giving them actionable intelligence by analyzing the data and categorizing it so that actionable insights are available to the companies in real time.

Predictive maintenance analytics has become number one choice of companies because of its efficiency in fault detection during early stages and thus reducing unscheduled downtime. Apart from this, It increases productivity, improves quality and provides the feeling of safety and reliability to staff. In process industries, such as steel, cement, power generation and others even a small downtime can lead to huge loses. A glitch in the production line in a car factory can put its production targets haywire. And this where predictive maintenance in plants is becoming almost a necessity.

What predictive maintenance analytics does is that it makes sense of the data coming from various sensors in the production line. By monitoring utilization of instruments, equipment condition is monitored, and internal component faults are identified, measured, and quantified.

This is vital for keeping the production process free of any malfunctioning components. Through this process, critical failure of mechanical equipment can be avoided while extending the life cycle of monitored equipment.

Moreover, machinery problems occur at specific frequencies, vibration analysis can pinpoint problems without guesswork. By the vibrations you can get the information you need for accepting new equipment, identifying problems for repair and after overhaul to assure machinery reliability. There are multiple cost benefits that come with predictive maintenance analytics. In this analysis, process sensors are placed on equipment and the data is collected, processed and alerts are generated based on the vibration pattern. As soon as some alert is sounded maintenance is scheduled during non-working hours and the machine is repaired before it fails. Thus, avoiding a complete breakdown and replacing the component even before it gives in.

Predictive maintenance by real-time data analytics allows potential problems to be fixed before failure occurs, which create safer conditions for employees along with increasing the revenue that would have lost due to complete breakdown. Instead of replacement of the entire piece of equipment due to critical failure, a repair can be made prior to failure and cost is minimized to the price of the component and the labor needed for the repair.

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