Everything you need to know to understand the value of predictive maintenance in industrial sector
Predictive Maintenance is a proactive technological method that tracks equipment performance in real time and predicts machine failure so that your company can fix it before it causes any downtime.
Predictive maintenance constantly analyzes the conditions of equipment during normal operation, detecting irregularities and immediately notifying the machine owner to decrease the likelihood of unplanned machine failure using real-time data received through the Industrial Internet of Things (IIoT), which is widely regarded as a key component of the Industry 4.0 revolution.
In this way, it dynamically monitors processes, detecting potential future faults and regulating them before they have any consequences.
Image by Keepler
Predictive maintenance vs preventive maintenance
Although often used interchangeably, there are significant differences between both predictive and preventive maintenance.
Preventive maintenance occurs at regular intervals based on the machine’s lifecycle, regardless of usage to ensure that no issues emerge
Predictive maintenance occurs when it is required based on the machine insights provided by the IoT sensors so as they wear over time, manufacturers can proactively schedule maintenance and avoid a costly, unexpected breakdown.
The difference between condition-based maintenance and predictive maintenance
Although both forms of proactive maintenance are aimed at preventing machine failure, there is a significant difference between condition-based maintenance; and predictive maintenance.
Condition-based maintenance (CBM) uses sensors to collect real-time measurements from a piece of equipment about various conditions, such as temperature, pressure, or vibration. Service is then delivered only once the condition status demands it -i.e. when your machine has hit a specified threshold parameter level.
While predictive maintenance is a type of condition-based maintenance, it uses the constant stream of IIoT sensor data on a much larger scale. Rather than only taking the condition status into account, predictive maintenance leverages big data methodology to predict machine degradation based on asset history and related data. Predictive maintenance allows technicians to catch potential issues even earlier, so service can be scheduled more efficiently. On the other hand, condition-based maintenance often runs the risk of multiple machines requiring service at the same time.
What role does predictive maintenance play in Industry 4.0?
Organisations cannot afford any breakdowns. It has an impact on the productivity and growth rate of businesses. A single loose bolt leads to excessive wear and tear, shortened equipment life, and unplanned downtime.
Smart technologies like big data, the internet of things, and machine-to-machine (M2M) communication methods are at the heart of the Fourth Industrial Revolution (4IR), which aims to provide you with optimal automation.
Predictive Maintenance (PdM) combines three of 4IR’s primary elements.
It employs real-time data intake to study the nature and extent of deterioration of an organisation’s amenities, extracts insights using machine learning algorithms, and generates reports on the machine’s current state and upcoming difficulties.
With the right PdM tools, you can successfully address such issues before they become a problem, saving time, money, and quality.
It provides a detailed report on a machine’s durability and shutdown time. This way, you’ll be able to receive a replacement in a timely manner and avoid any future work-related issues.
Cyber-Physical Systems are at the heart of Industry 4.0. (CPS). Computer-based algorithms guide the consolidation of physical and software components in CPS.
Predictive Maintenance Benefits in the Fourth Industrial Revolution:
Expenses are lower.
Every piece of equipment is monitored.
Predictive Maintenance efficiently manages and monitors large-scale industrial units. PdM tools give your company a clear roadmap to deal with the increasing volume of work.
You may obtain an accurate picture of your equipment’s health and the causes of production disruptions by deploying condition-monitoring sensors.
Advanced Analytics Applications Power Predictive Maintenance Efforts
Implementing this type of predictive maintenance strategy requires a thorough understanding of how and why assets fail, as well as early detection of potential problems or failures.
Many of these criteria are hidden in an organisation’s existing process history data. Along with previous preventive and reactive maintenance efforts, predictive maintenance provides plant personnel with the knowledge needed to save costs and maximise uptime. According to usual approaches, one certainty is that in, order to make the transition from preventive to predictive maintenance, a wealth of clean, contextualised data and its proper application is imperative.
A predictive asset health model should take into account as many variables as possible, as well as predict the useful life of an asset, to define maintenance programs. A well-executed approach will reduce costly downtime and provide adequate preventive maintenance programs, but it is easier said than done, and some solutions are more effective than others.
This is why Keepler initiated a deep learning and generative models (GANs) approach, with the goal of generating new images from a limited dataset provided by the customer. In this way, projects presenting a reduced dataset and with current well-functioning data could be made viable. The solution developed by Keepler would remove this barrier, early results are promising, although the process still needs to be advanced to produce a viable solution using more complex modelling approaches. The only barrier to innovation, ultimately, is time.
Utilise IoT-enabled predictive maintenance to gain a competitive edge
Predictive maintenance offers many benefits that invest in the IIoT, from reduced downtime and fewer productivity lags to cost-savings advantages. The benefits of predictive maintenance will extend beyond your service department to become enterprise wide. Don’t wait until machines are down to fix them. Start being proactive at your plant with predictive maintenance and optimise your workforce efficiency. For those companies that want to improve their service and save costs and need a fully customizable solution to give the best answer to performance and maintenance issues, Keepler provides a cloud-based solution that relies on machine learning in order to continuously optimise the predictive algorithm.
Technical Writer at Keepler. "I've been a technical writer and instructional designer for different industries for a decade now and I still haven't stopped learning. When I'm not reading and writing about new methodologies you can find me writing science fiction."
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