Predictive Maintenance: Advantages and Implementation

“The goal is to turn data into information and information into insight.”

- Carly Fiorina

 

After an analysis of the worldwide predictive maintenance market, Market Research Future made a predictive maintenance report that projects that it will reach $23 billion by 2025. The manufacturing industry has the biggest number of deployments, but all companies who have significant capital invested in their equipment are particularly interested in predictive maintenance.

 

One must first comprehend what predictive maintenance is, how it functions, what its benefits and drawbacks are, and how one goes about putting one in place before deciding if it is a plan that your company could benefit from. You will learn more about Predictive Maintenance in this blog.

 

What is Predictive Maintenance?

 

Utilizing condition monitoring instruments, predictive maintenance is a maintenance method that finds numerous symptoms of abnormalities, deterioration, and equipment performance problems. 

 

Based on those measures, the organization can use predictive algorithms that have already been established to determine when a piece of equipment might stop working so that maintenance can be done immediately before it happens.

 

Making the best use possible of current maintenance resources is the aim of predictive maintenance. Maintenance managers can avoid unnecessary maintenance while averting unplanned equipment breakdowns by scheduling maintenance work only when it is strictly necessary. This is possible because they can predict when a specific part will fail.

 

When properly implemented, predictive maintenance decreases downtime problems, reduces operational costs, and enhances the overall health and performance of the asset.

 

Predictive maintenance is sometimes confused with Preventive Maintenance. Preventive maintenance (PM) is the routine maintenance performed on equipment and assets to keep them in good working order and avoid expensive, unplanned downtime due to unanticipated breakdown. 

 

Before a problem arises, scheduling and planning equipment maintenance is essential to a successful maintenance approach. A solid preventative maintenance strategy involves keeping track of previous inspections and equipment servicing.

 

Also Read | Predictive Analytics: Techniques and Applications

 

Components of Predictive Maintenance

 

The primary benefit of predictive maintenance is the ability to plan work based on the asset's existing state. Yet it is far from simple to determine the precise state of complex assets.

 

For tracking asset condition and alerting technicians to impending equipment breakdowns, Predictive Maintenance relies on three key components:

 

1. CM technology and Preventive Maintenance Methods

 

Numerous sensors and pieces of equipment for condition monitoring can be installed or retrofitted. Electrical currents, temperature, vibrations, oil, pressure, noise, corrosion levels, and more can all be measured. The sensors you choose rely on the assets that will be on your Predictive Maintenance and what you want to track.

 

Using condition-monitoring sensors also gives you an accurate picture of what is going on inside the asset without interfering with the  productivity. In other words, performing physical inspections does not require stopping the asset and taking it apart.

 

There are several condition-monitoring/predictive maintenance strategies available, depending on the sensors you employ and the tests you wish to do.

 

• Lubrication and oil analysis
• Dynamic monitoring and analysis of vibration
• Circuit analysis of a motor
• Thermography in a variety of forms
• Analysis using ultrasound and sound
• X-rays and radiation analysis
• Laser interferometry
• Electromagnetic analyses
• Many performance metrics

 

2. Role of IoT Technology

 

The ability to interpret and apply the data for the intended purpose is much different from simple collection of the data. The sensors that were previously discussed can gather and share data by utilizing Internet of Things (IoT) technology. 

 

The connection between the assets and the central system that stores the incoming data is made possible by Predictive Maintenance due to these sensors. LAN-based or WLAN-based connectivity, as well as cloud computing, are used to power these central hubs.

 

Depending on how the system is configured, the assets can then interact, analyze data, collaborate, suggest corrective action, or act on their own.

 

3. Applying Predictive Algorithms

 

The creation of predictive (also known as prognostic) algorithms is the key component and also the hardest component of predictive maintenance. To be able to forecast machine failures, a model must be constructed in such a way that it takes into account a wide range of factors and how they interact.

 

The models will be more precise when more variables are used. Because of this, the process of creating predictive models is iterative. 

 

The initial models must be based on asset history data kept in a CMMS or file cabinets, FEMA analysis, individual observations, currently available internal sensors like flow meters and accelerometers, and sources similar to these. 

 

To collect baseline data and to complete the initial prediction models, condition-monitoring sensors may be installed and operated for a while.

 

The deployed sensors will produce an increasing amount of data over time, which can be utilized to enhance the initial models and produce almost perfect failure predictions.

 

The algorithms can also function without getting too technical. They adhere to a set of established rules that contrast the asset's present behavior with its future behavior. Deviations are a sign of slow degradation that eventually result in asset failure. 

 

The algorithms attempt to anticipate failure spots based on present operating circumstances, deviations, historical failure data, and any other variables included in the data model.

 

As a result an automated system is formed that:

 

• Analyzes operational status using mounted sensors.
• Recognises and foresees patterns caused by data anomalies.
• Produces warnings whenever there is a departure from predetermined thresholds.

 

 

Pros and Cons of Predictive Maintenance

 

The pros of Predictive Maintenance are given below :

 

1. Optimizes asset uptime and minimizes the occurrence of unplanned downtime.

 

2. Provides you with a current, real-time picture of the state of your assets.

 

3. Minimizes productivity disruptions because some predictive maintenance procedures can be undertaken out on active assets.

 

4. Maximizes the time spent on maintenance tasks.

 

5. Makes best use of spare parts.

 

6. Makes an asset more reliable.

 

The Cons of Predictive Maintenance include :

 

1. Needs equipment for condition monitoring to be put into practise.

 

2. Understanding and analyzing the condition-monitoring data requires a unique set of abilities.

 

3. High up-front expenses.

 

4. Can be time-consuming to set up and implement.

 

Implementation of Predictive Maintenance Program

 

Building a sustainable system requires laying the framework for Predictive Maintenance. The goal is to begin modest and increase as the organization becomes accustomed to this new method of working.

 

The typical procedures a company should follow while implementing a predictive maintenance programme in place are given below.

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Predictive Maintenance Implementation

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1. Secure the Budget

 

Predictive maintenance implementation takes time to complete. Prior to making any decisions, one must have senior management support and assurances that the project will receive adequate funding.

 

2. Identify Critical Assets 

 

To begin, one must decide which assets are essential to include in the Predictive Maintenance programme. The best prospects are frequently those assets that are essential to production but have significant repair and replacement costs. 

 

Whether an asset is worth adding to a predictive maintenance programme or not, can be determined by conducting Revenue Cycle Management (RCM) analysis.

 

3. Establish a Database

 

Another aspect to take into account for the Predictive Maintenance program's effectiveness is whether there is enough data to give useful insights into how machines behave. 

 

Historical data will be accessible for each piece of pilot equipment from a variety of sources, including hard copy files, CMMS, enterprise software from other departments, technician personal experience working on the assets, maintenance records and charts, etc.

 

When creating the initial iteration of the prediction algorithms, this data may be helpful in determining failure modes.

 

4. Analyze and establish failure modes

 

The organization now has to determine the failure modes of the important assets that were previously identified. They can determine which failures are the most problematic and have more possibility of actually occurring using this technique.

 

5. Implement Condition Monitoring Equipment

 

The business can buy the right technology and sensors to monitor the parts that are most likely to fail by knowing which failure modes they need to be on the lookout for. There are different condition monitoring methods and tools available for this function.

 

Be aware of outdated equipment because it might be difficult to adapt it along with contemporary sensors. Before you invest in condition monitoring technology, one should make sure that it is possible to do so.

 

6. Develop Predictive Algorithms

 

Designing the appropriate modeling strategy that will serve as the foundation for failure predictions comes after the following steps. At this stage, it is anticipated that the firms will employ data scientists to create predictive maintenance algorithms that are based on sensor measurements and other information that the organization was able to offer.

 

7. Deploy to Pilot Equipment 

 

The deployment of the technology to a chosen group of pilot pieces of equipment is when the predictive modeling is put to test and validated.

 

Operations for the organization will significantly improve if the process is followed according to plan. Depending on the size of your operations and the amount of machine downtime experienced before the installation, noticeable effects can take a few months to manifest.

 

Also Read | What is Predictive Modelling?

 

Applications of Predictive Maintenance in Different Industries

 

Predictive maintenance will become more widespread as technology costs continue to decline, partly as a result of the transition from wire-based to wireless sensors. The most common industries where predictive maintenance is used are given below.

 

1. Predictive Maintenance in Manufacturing Industry

 

The advantages of Predictive Maintenance's greater reliability are highly alluring, and manufacturers are beginning to take notice. By estimation as per the predictive maintenance report by Market Research Future mentioned above, manufacturers may save anywhere between $240 and $630 billion worldwide by utilizing predictive maintenance by 2025. 

 

Monitoring robot CPU and housing temperatures, as well as orientation and overload faults, and utilizing this data to estimate a robot's health are just a few examples of the many practical applications of predictive maintenance in manufacturing.

 

2. Predictive Maintenance in Transportation Industry

 

The number of connected transportation devices is rising as more automakers build cars with sensors that can collect data and transmit information about the machine's operation. This information can be used by fleet managers to make plans for vehicle maintenance.

 

Through these procedures, on-road breakdowns, cost, and technician diagnostic time have been significantly decreased. Predictive maintenance is becoming more popular among fleets for a variety of reasons, lower fuel and maintenance costs, including less unscheduled downtime, and quicker servicing.

 

3. Predictive Maintenance in Aviation Industry

 

To lessen the expensive consequences of maintenance-related aircraft cancellations and delays, aviation businesses have developed and continue to employ Predictive Maintenance. 

 

By analyzing multiple temperature and vibration readings, predictive maintenance is also employed in the industry to assist airlines in monitoring engine performance and health during flights.

 

Also Read | Best Predictive Analytics Tools

 

The industries that have started using Predictive Maintenance technology apart from those listed above include those in mining, healthcare, electric power, oil and gas, and construction.

 

Energy-related businesses have particularly significant exposure risks, which is why it is predicted that their use of predictive maintenance technology will increase the fastest in the energy sector.

 

Predictive Maintenance is a method that offers significant benefits, but it is without doubt not the most straightforward to put into practice and manage. Predictive Maintenance is fantastic for asset-intensive enterprises that are willing to embrace contemporary technologies and with the capacity to perceive the broad picture. 

 

Even though the initial expenditures may be substantial, it is an excellent long-term option that can offer a high return on investment (ROI) through enhanced machine performance and cost savings.