Predictive Maintenance for Heavy Machinery

The Problems Commonly Faced in Maintaining Heavy Machinery

Maintenance work is a common practice in any heavy machinery industry. It is a standard procedure needed to ensure that machines operate and function normally with minimal breakdowns. However, when machines do break down, it may result in operational downtime, incurring severe costs. Moreover, workers then have to deal with the breakdown, preventing them from focusing on improving production and productivity. In fact, 82% of companies have experienced unplanned downtime. And this can cost a company as much as $260,000 an hour.

Many companies are still relying on a reactive and time-based maintenance approach, where maintenance work is only done after a machine has broken down. The downside to using such an approach is the loss in production time and the heavy cost incurred. Studies have shown that an average of 4 hours of downtime translates to an average cost loss of $2 Million. Imagine all the monetary losses that you have incurred so far. 

Those who rely on a preventive maintenance approach are not better off either. Although carrying out maintenance works in pre-scheduled routines reduces the occurrence of breakdowns, it does not consider the actual state of the machines and good-conditioned machinery may still be scheduled for maintenance even when not needed. This is not an efficient use of resources. In fact, it is a waste. 

However, what if there was a way to reduce this operational downtime?

What if there is a way to reduce the cost associated with this downtime?

What if we tell you that there is a way for you to predict future machinery breakdowns?

In actual fact, you can predict when a machine will break down with Predictive Analytics.

What is Predictive Analytics?

Predictive Analytics is an aspect of Data Analytics that makes predictions about the future outcomes based on analyzing historical data and using Machine Learning to derive trends. Predictions made generally have a high degree of accuracy, allowing users to rely on the predictions made to make future decisions. 

With the vast amount of data available and efficient processing, past and present data can be easily monitored to forecast trends and outcomes within a matter of seconds. Various techniques are used to generate predictions, such as Data Mining, Machine Learning, and Statistical Modelling etc.

With Predictive Analytics, you can identify trends and patterns, therefore easily deriving future opportunities or risks. This allows you to forecast future machinery breakdowns and problems, giving workers the early opportunity to carry out maintenance work before damage is done. Predictive Analytics powers Predictive Maintenance, the solution to your machinery maintenance.

How does a Predictive Maintenance System work?

Stage 1: Detection and Monitoring of Performance

First and foremost, in order to make a prediction of future machine breakdowns, data is needed to be processed and analyzed. Data will be obtained from condition monitoring equipment attached to the machinery. This replaces the need for physical inspection since data is captured automatically, saving workers’ time and enhancing workplace safety.

Common data, such as vibration, temperature and sound made by the machines, will then be uploaded onto a centralized database for analysis in the later stage. Customized measurements can also be made to cater to machinery of different types, depending on the business’s needs.

Stage 2: Troubleshooting

Troubleshooting is the process of finding the source of the problem and fixing it. Using Predictive Modelling algorithms and the data obtained through the sensors, troubleshooting of potential maintenance issues can be automated and done in real-time to prevent under-performance of the machinery. In addition, Machine Learning using historical breakdowns can be used to further improve the accuracy in detecting the root cause of an issue without being explicitly programmed.

Stage 3: Data Analysis and Prediction

Now here comes the magic of making the prediction as to when a machinery will indeed break down. Combining Predictive Analytics and data together, the system is able to simulate the manual process of determining the necessary maintenance of a machinery. Based on past trends observed, the system can accurately predict the likelihood of a machinery malfunctioning and prompts are sent to warn of potential malfunctions so that maintenance works can be quickly arranged.

Scheduling Maintenance Works

After detecting the fault, repair and maintenance works come naturally after that. However, manual scheduling of maintenance works is not only time consuming, but runs the risk of incorrect scheduling.

How to ensure a faster process and higher degree of accuracy in scheduling maintenance works then?

Automation is the key. With AI automating the maintenance works, a large amount of man-hours can be saved and workers do not have to worry about scheduling the wrong maintenance. Only the required machinery which requires maintenance will undergo a robust scheduling, taking in multiple data and considerations (not limited to):

  • Urgency of maintenance
  • Production delivery time
  • Duration of maintenance
  • Ability of buffer to embrace duration of maintenance
  • Operating hours of equipment

Overall, optimal maintenance work is scheduled without disruption to the operational process.

The Benefits of Using a Predictive Maintenance System

Human judgments will never be 100% accurate in predicting a machinery breakdown. However, a Predictive Maintenance System is able to forecast future breakdowns more accurately than any other tools due to its ability to analyze large amounts of structured and unstructured data.

With trends and patterns identified easily, scheduling maintenance work in light of a machinery breakdown will be a breeze. This saves a lot of cost on unnecessary repair works and can severely reduce operational downtime as compared to taking action only after a breakdown has occurred. If this system has the ability to upscale your machinery operations, why not leverage it?

Take Advantage of Predictive Maintenance

Maintenance work is part and parcel in any machinery industry. Regardless, minimal breakdowns should be achieved to ensure a smooth operation. Without a doubt, using Predictive Maintenance to reduce unexpected downtimes and maintenance costs for heavy machinery brings a slew of benefits in the long run. Implementing it would definitely make exceptional improvements to all-round productivity and profitability for your business. However, we know that having such a system may be overwhelming for your team, especially if this is a completely new concept to them. This is why you need an expert to help you with the implementation.

At groundup.ai, we will support and guide you through the process of implementing the system into your daily operations. Using Predictive Analytics, we can efficiently pull data and behaviors from your machinery to create evolutionary changes to your business and use innovative solutions to drive business value. 

Have a chat with us today to see how we can customize the Predictive Maintenance System based on your needs and the benefits it can bring to your business.

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