Being able to foresee faults and find a solution to them before they spiral into bigger problems is indispensable, as it is to conduct industrial predictive maintenance. At the same time, the equipment is still functional is also a good way to avoid interrupting any operations. Recent research shows that unscheduled downtime costs the industrial sector around $50 billion yearly. Measures such as IoT-based predictive maintenance and AI-driven analytics have changed companies’ outlook on production line management and equipment maintenance.
In this article, we take you through the basics of lot-based predictive maintenance and how it can improve the value of your business.
What is Predictive Maintenance?
Predictive maintenance is said to be a technique that employs data analysis tools and additional techniques to figure out the anomalies that may be present in your operation and any defects in processes and equipment to allow you to handle them in time. In an ideal situation, predictive maintenance lets the maintenance frequency be as low as possible to avoid unexpected reactive maintenance.
Components for Predictive Maintenance
Predictive maintenance uses real-time and historical data from different parts of your operation to anticipate issues before they disrupt your processes. There are thus several key elements as far as predictive maintenance is concerned, with software and technology being some of the more critical components.
Other components are the internet of things(IoT), integrated systems and artificial intelligence. All these components make it possible for different systems and assets to be connected and to work together to share, analyze and provide actionable predictive analytics for maintenance.
These components of predictive maintenance acquire information using predictive maintenance sensors, business systems like ERP software and industrial controls. They then use this information to identify the areas that require attention. Some examples of predictive maintenance components in action are oil analysis, vibration analysis, equipment observation and thermal imaging.
Advantages of Predictive Maintenance
There are many advantages to be gained by adhering to a preventive maintenance model, such as;
1. It Accounts for the Actual Condition of Your Systems
By going through the performance and overall condition of your equipment, you ensure that the actual needs of your equipment and processes as far as maintenance is concerned are met.
2. Early Detection of Faults
One of the major advantages of preventative maintenance is the early detection of faults that would otherwise disrupt the entire system.
3. Less downtime
This is achieved since the required maintenance is only performed when needed. In addition, you will experience less downtime when faults are detected before they become worse and grind the entire operation to a halt.
4. It Extends the Lifespan of your Equipment
This is achieved by stemming any problems to your equipment in its infancy before they cause further disruptions.
How Lot-Based Predictive Maintenance Generates Business Value
1. Improving overall equipment life
Traditionally, qualitative and quantitative metrics have been applied to foresee problems and reduce downtime. Predictive maintenance can also raise your company’s value by helping you maximize the life of your equipment.
Data generated by IoT sensors can provide your company with a comprehensive view of your equipment’s health, enabling you to schedule repairs and prioritize them, resulting in better quality and more optimized performance.
2. Enhancing the Quality of Production
Predictive maintenance will enhance production quality due to its ability to detect errors in real-time. When issues that affect your equipment and systems are dealt with proactively, the factors that affect productivity, such as costs, safety concerns and downtime, will no longer be a hindrance. Maintenance teams can elevate the value of your business and improve efficiency by preemptively lining up repairs instead of only reacting to the equipment once it breaks down.
3. Reducing Maintenance Costs and Downtime
The ever-improving state of IoT-based maintenance gives operators the capacity to be more proactive regarding identifying faults in equipment. For industries driven by equipment, historical data derived from IoT devices and other sensors can provide clear information on machine usage and possible risk areas.
Even in the absence of historical data, predictive Ai models can help generate a framework that will simulate real-world scenarios meaning that you can still cut down on downtime and maintenance costs.
4. Finding Areas of Inefficiency
Sensor data analytics, in conjunction with advanced algorithms, can reliably generate leads regarding various equipment. Expedient and real-time insights are very important to detect inefficiency early on and even improve these areas, increasing your business’s value in the process. For example, insights drawn from an IoT-based sensory structure can be boosted by AI solutions to open up channels for a power company to improve its production grid and other benefits. In addition, potential health risks, compliance problems and potentially hazardous situations can be identified in time and addressed.
5. Expedient and Better Informed Decision Making
IoT devices can double up as real-time data sources anytime and from any location. A predictive maintenance platform will allow your business to make timely decisions since you will have all the information you may need.
Data insights collected by manufacturing analytics and censors will paint a clear picture of faults, areas that can be improved and other performance markers that will allow managers to narrow down their focus, increasing the value of your business in the process.
As a business owner, it would help if you take advantage of predictive maintenance and apply it to your business to reap the numerous benefits of the entire process and propel your business to the next level.