Predictive Maintenance with Machine Learning – 2020 Top Opportunity for Your Manufacturing Business
Being on the verge of the fourth industrial revolution, modern business and production are forced to solve a whole range of problems that they have not encountered before. These are constantly increasing customer requirements for product quality, more stringent requirements of contractors for the reputation of suppliers and the ability to fulfill the terms of the contract on time or even faster, the growing need to optimize and reduce costs to increase profits and reduce the harmful effects of production on the environment. Artificial intelligence, machine learning, predictive analytics, and maintenance can handle a complex of these tasks. In this article, we will look at a promising combination of Predictive Maintenance with Machine Learning and find out what benefits these technologies can bring to your production and business as a whole.
Predictive Analytics and Machine Learning
By 2022, the market and the request for predictive service solutions will increase by seven times, or to $6.3 billion in monetary terms. This will become one of the main trends in technological modernization. These are impressive numbers, but before deciding on the need for Predictive Maintenance with Machine Learning for your manufacturing business, let’s delve into the details and understand how it works and what horizons it opens.
How Does Predictive Maintenance Work
Let’s start with a simple example. Suppose you have some simple equipment, such as a pump. Like any equipment, it needs maintenance and timely replacement of parts. Each part, in turn, has such an indicator as the average life according to technical specifications, plus a certain margin of safety. When you have only one simple mechanism, then yes, you can certainly catch the moment when it is necessary to think about replacing a particular part and do it in advance – before the part completely fails and the pump from our example stops working.
But if you have thousands of units of similar equipment in your production, it becomes obvious that it is unrealistic to predict neither planned replacement of parts nor unplanned breakdowns. At least using the abilities of the human brain only and processing information manually. At this point, the need for the introduction of IoT devices arises.
However, let’s not complicate our example. Suppose you have installed such a device to track the operation of your pump. What does it do? First of all, the sensors of this device will “catch” a certain parameter, for example, a certain temperature or the power of the equipment. Further, this data will be transmitted to the field gateway – this is the first place of data storage, after which the data will be sent to the cloud gateway and then to the data lake. This lake is a collection of all the information about all your equipment. In other words, this is unclassified, dry data, just a set of information that is not valuable until it is classified and analyzed. And at this moment, the machine learning system should be included in the work.
The machine learning model will be trained in such a way as to recognize parameters and signals that are fundamentally important for your business. In other words, it is a kind of custom system that focuses on the analysis of data relating to your production and is trained to make assumptions about planned (for example, the need for a planned replacement of a particular part) and possible emergency situations.
Why Predictive Maintenance Is Important
So, what benefits can you get for your business from implementing predictive maintenance technologies?
- Cost reduction. According to various estimates, predictive maintenance is able to reduce your costs up to 50% mainly due to timely response.
- Reducing the risk of lost profits. Every second, when your production does not work for your benefit, you lose potential profit.
- The decrease in reputation risks. If your contractors are waiting for your products for a week, and at this time you are repairing your equipment, having failed to foresee the probability of its unexpected breakdown, this does not affect your reputation in a bad way.
- Improving the environmental friendliness of production. Obviously, when your equipment begins to work beyond its technical capabilities (this is what is called “wear and tear”), then the mechanisms begin to emit more harmful emissions than allowed by environmental standards. In today’s environmental realities, this is an unacceptable situation, and if you are striving for the environmental friendliness of your business as well, then predictive maintenance technology is the thing that is able to help you.
In addition, here are some pieces of statistical data that also speak in favor of predictive maintenance for your production business.
- 80% of maintenance personnel believe that this technology is unprecedentedly effective;
- 80% of manufacturing plants have already introduced this innovation, and 50% of them use it in combination with analytical tools to get more insight on how to improve production lines;
- according to general predictions, it is possible to improve a plant’s capacity and productivity by 10% and 50% respectively with the introduction of predictive maintenance technology.
Predictive Maintenance Use Cases
Let’s see how predictive maintenance technology can be implemented in different industries.
- Food industry
Having the ability to predict equipment breakdowns, food manufacturers insure themselves against the biggest possible costs – expenses due to spoilage of raw materials.
- Automotive industry
At the moment, many manufacturers of high-tech cars are introducing this technology, which is able to warn the car owner about a possible breakdown before this happens.
As one of the safest industries, aviation still needs predictive maintenance. This will make air transport even safer, and passenger traffic even more timely.
- Power and energy industry
In this case, we are talking about a huge number of details and mechanisms that help to provide the population with energy resources. The ability to deliver these resources without any interruption becomes a key requirement for the most important facilities such as hospitals and transportation hubs.
What Is a Predictive Maintenance Strategy
A predictive maintenance strategy is part of the business strategy. And if you still have not decided to implement this technology in your production processes, then right now is the best time to do it.
Planning for this strategy begins with key performance indicators that should be taken under control. Further, it will be necessary to determine which IoT device should cope with this task and configure the appropriate data transfer system from production equipment to the machine learning model, followed by the transfer of the processed data to an application available to the owner and employees of the enterprise.
How to Start a Predictive Maintenance Program
So, to begin the process of introducing predictive maintenance into your business processes, then, as we have said, you need to start with the key indicators of your equipment that you want to monitor, analyze and build assumptions based on them.
Further, it made sense to contact a specialized company that has experience in introducing these technologies into production processes and receive the first consultation. Experts from advanced companies, such as how the SPD Group will analyze your current situation, ask for all the available data on the history of your production, make a preliminary assessment and the first sketches of a machine learning model that suits you based on your tasks.
Artificial intelligence, machine learning, the Internet of things and predictive analytics are the future not only of production and business but of humanity as a whole. This is a process that can no longer be stopped or reversed, so the best thing you can do is to take advantage of the benefits that innovation can give you and introduce modern technology into your business processes today.