What is machine learning and how does machine learning work with predictive maintenance?
Machine Learning Uses Data to Improve Businesses
Machine learning allows for more intelligent ways of processing data to predict when an asset will require maintenance. This allows for a more efficient allocation of resources in performing predictive maintenance.
With the emergence of the Internet of Things (IoT), the ability of everyday objects to collect and transmit data is done more easily than ever. The frequency of collecting data points and the number of assets for which data can be collected is no longer limited by the human capacity to do so.
Machine Learning Boosts Predictive Maintenance
While predictive maintenance already utilizes available data to predict failure outcomes, machine learning takes this a notch higher by applying algorithms and statistical models to available data – thereby allowing more exhaustive methods of predicting failure scenarios.
Machine learning works by employing several available learning algorithms that interprets historical data to predict future outcomes. One of the most commonly applied learning methods is through the use of regression models – that is taking the graphical representation of historical data to predict future outcomes given similar conditions.
How does machine learning through regression work?
At a high level, machine learning is able to take historical data and identify the parameters that precede certain failure outcomes. Performance and operational data that are continuously being collected by installed sensors can be plotted in graphs over time. For example, given a certain duration of time, the performance of certain equipment can be logged and plotted. Given a plot over time, regression models can be used to predict the factors that can cause failure events that have already previously occurred.
Is machine learning for me?
Knowing how machine learning works and the conditions that make it applicable give you an idea on which parts of your plant would benefit from it more than others. Some key points that can help you assess whether machine learning is applicable are the following:
Data quality
Do you have the right data to predict specific outcomes? Is you data clean and validated? Do you have enough data points that can be trained to provide useful predictions?
Machine learning platform
Which machine learning platform is most applicable to your operations?
Data scientist resource
Do you need a dedicated data scientist that can be integrated into your operations?
Sharing the data output
Are you able to share and scale the information from one asset across the whole plant?
What’s the difference between machine learning, AI and deep learning?
The difference between machine learning, AI, and deep learning is best summed up in the difference between the three rings of a bulls-eye target. AI, or artificial intelligence is the first outer ring, followed by machine learning, which circles the bulls-eye center or deep learning.
In order to understand these differences, let's have a look at the definitions of each one and how they overlap today.
The definitions of machine learning, AI, and deep learning
- AI, or artificial intelligence: when machines strive to perform tasks as a human would
- Machine learning: when machines mimic human behavior and human learning styles, improving through experience
- Deep learning: Imitates the human brain, involving neural networks
Some main differences between machine learning, AI, and deep learning
The main difference between these three types of artificial learning is that they all nest inside of each other. Machine learning falls under the heading of AI and deep learning falls under the heading of both. This may explain why they are so easy to confuse.
Another major difference is how much and in what field one can be used. For example, machine learning, and AI are both commonly used today in many different applications. Some of the most exciting developments are in the field of maintenance in the form of systems such as sensors, the Internet of Things, and more.
One shining example among many of how machine learning and AI are being used in cyber-physical systems and maintenance applications. In ordinary preventive maintenance systems, new sensor networks leverage the Internet of things to bring companies greater clarity into their everyday maintenance.
Deep learning, on the other hand, is reserved for experimental and brand-new systems, technologies, and research that isn’t widely available to the public at present. While it has great potential for the future, it’s simply not in a state that can be used commercially in a wide variety of applications.
How companies are applying machine learning, AI, and deep learning
Finally, the practical difference for most companies between machine learning, AI, and deep learning is that they can use machine learning AI today in many different applications. These include work orders, company books, payroll, inventory management, and much more.
And this capacity will only increase in the future.
What are some uses for Machine Learning/AI in Industrial Applications?
In today’s competitive environment, there are many uses for machine learning and artificial intelligence in industrial applications. These include automation of all sorts, intelligent sensors, increased analytical insights, higher returns on investment, and more.
In order to understand the applications of machine learning and artificial intelligence, companies have to understand what the systems are capable of today. Let’s start with some initial definitions and parameters around machine learning and AI before we dive into the four main uses for it in industrial applications.
The definitions used in this article are clearly stated below.
Machine Learning: The use of computational methods in the optimization of machine performance.
Artificial Intelligence: Any and all applications of artificial intelligence that apply to the physical operations and/or systems of a company.
With that out of the way, what are the top four uses for machine learning/artificial intelligence in industrial applications today?
- Streamlining predictive maintenance.
- Boosting company productivity.
- Optimizing overall operations.
- And increasing your employees’ health and safety.
Here’s an in-depth look at each of these four uses and how they are benefiting companies today.
1. Streamlining predictive maintenance
Perhaps the clearest form in which artificial intelligence assists companies and their predictive maintenance strategies is in the industrial Internet of things. When systems are used, they can dramatically boost and streamline industrial maintenance in general and predictive maintenance, in particular.
This is because this type of maintenance is dependent on sensor networks. Sensor networks that utilize artificial intelligence and machine learning within the system of the interconnected devices and in the devices themselves are much more efficient than sensor networks that do not utilize similar methodologies.
2. Boosting productivity
When employees are freed up from repetitive, simplistic, or boring tasks that are integral to the company, productivity generally rises. This is because when workers are given tasks and jobs that have meaning, they become more invested in the company. It also enables companies to put employees where they are needed most and not just where tasks need to be done.
On a slightly darker note, when companies use artificial intelligence, they don’t have to hire people to do those jobs anymore. Artificial intelligence and machine learning may cost more upfront, but in the long run, they are less expensive. While this can lead to long debates on the state of the economy and of the job market, the fact is that when companies can hire fewer people to get the same jobs done, this boosts productivity.
3. Increasing your employees’ health and safety
This is not an intuitive point to make about machine learning. Of all the things it can do, increasing health and safety is not high on the expected list of results. However, when companies look at automating dangerous and repetitive work, this bounces back in. The result of increased health and safety.
Artificial intelligence, in particular, is quickly becoming the perfect companion for safety managers in fields such as construction, manufacturing, and roadwork. It can accompany safety professionals in the monitoring of the employees while remaining cost-effective and affordable. In some cases, it can add safety measures to areas and teams that did not have them before at a much lower cost that is otherwise possible.
4. Optimizing overall operations
The overall operations of a company go far beyond optimizing maintenance and general productivity. They cover a multitude of such things as routine maintenance, bookkeeping, employee engagement, and countless other minutiae. This is where the Internet of Things can shine brightly.
What are some concrete ways in which machine learning and AI optimize industrial operations? First, they offer computer-based vision that can be applied to many different areas. When it’s used with sensors, it can give you real-time updates into the workings of your machines when it’s used on the analytical side, it can point the way toward stated trends, fault patterns, and other data sets that may not otherwise be found. It’s no secret that computers can catch things that humans miss on a regular basis, and computer-based vision is a great example of this.
Next up is the sheer computing power that they offer companies. What used to take a team of highly skilled professionals can instead take computers days or even hours depending on the scope of the project and the time devoted to it.
Finally, when the virtual world is paired with the physical world, companies are able to tap into multiple different points of view and aggregate them into a big picture, high level, overview of their current situation.
And that’s perhaps the most powerful use of machine learning and AI in industrial applications today.
Want to keep reading?
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What is Machine Learning?
Machine learning is a type of artificial intelligence that allows a computer to take existing data, experience, and information, identify patterns, and draw new conclusions and take action without human intervention. It uses a mathematical model that takes a data set as a training ground and then makes future decisions without a programmer’s direction.
Types of Algorithms
Four types of machine learning algorithms are available today.
Supervised machine learning algorithms use existing data sets to anticipate what will happen in the future. After reviewing past information, this type of machine learning can help determine what might happen later, as well as ways to prevent undesired outcomes. On the other hand, unsupervised machine learning uses disorganized data to find patterns and structures that are not yet identified.
Semi-supervised algorithms are a mix of the two above, usually with more unstructured data, and is helpful in situations where the small set of labeled data requires some management. Reinforcement algorithms work by learning from their environment. This type of algorithm uses trial and error and chooses future actions when positive feedback is acquired.
Industry Applications
Many industries are already using machine learning technology. For example, machine learning algorithms can help healthcare businesses track a person's health, as well as help medical professionals identify trends in illness and disease.
Machine learning can also help the oil and gas industry find new sources of energy and predict equipment failure before major spills occur. Within transportation and fleet management, machine learning can help companies make travel routes more efficient and reduce fleet maintenance costs.
How to Create Strong Machine Learning Systems
Today's technology and the sheer volume of data that is collected and available make machine learning a viable solution for many organizations in the near future. Machine learning can help businesses build accurate models, find new opportunities, as well as minimize safety, health, and environmental risks.
In order to create a strong machine learning system, you need to have a quality system to collect and store data. Learn about both basic and advanced algorithms, as well as automation processes. Once those items are in place, you can create some models to test your machine learning systems and then scale as needed.