Predictive Maintenance with Machine Learning: A Complete Guide

Introduction

Nowadays all major hardware, solution, and service markets rely on top-notch modern equipment and devices for their faultless functionality. Thus, the cost of failure may be huge for the organization if the measures to prevent it come too late. Some people choose preventive maintenance or even reactive maintenance, which is the most dangerous for revenue, but as the world heads toward Artificial Intelligence integration, good leaders choose Predictive Maintenance.

The technology of real-time streaming is gaining more popularity, and this is the main reason why the Predictive Maintenance market is growing. Real-time data is streamed from devices, sensors, and mobile applications, which then underlies analytical computations. Streaming analytics, being one of the essences of Predictive Maintenance, delivers real-time data to systems that perform automated monitoring, with the intent to preserve asset health or for staff to know when maintenance measures should be taken.

Market Research Future on Predictive Maintenance claims that the market will increase by at least 25% CAGR and reach $23 million in 2025. In factories, Predictive Maintenance is regarded as the most useful application for the Internet of Things. A CXP Group report says that 90% of manufacturers who implemented Predictive Maintenance in their work noticed reductions in repair time and unplanned downtime, while 80% saw that their old industrial infrastructure was improved. Machine Learning in Manufacturing is a big deal, and one of the most promising industries for AI solutions development.

Here you will find out everything linked to what Predictive Maintenance is, including use cases and the role Machine Learning plays in it.

Predictive Maintenance services

Predictive Maintenance services are driven by predictive analytics. The first purpose of this technology is detecting and supervising anomalies and failures in equipment, which prevents the possibility of critical failure and downtime. This enables deploying restrained resources, increasing device and equipment lifecycles, while advancing quality and supply chain processes and increasing the general satisfaction of stakeholders.

Predictive Maintenance for Analytics

Large amounts of data are collected, stored, and processed so that Predictive Maintenance for Analytics can be performed. This data usually includes the condition of the equipment, vibration, acoustic, ultrasonic, temperature, power consumption, and oil analysis datasets, as well as data from thermal images of the equipment. Data Collection, however, is only the first step, Data Mining and Machine Learning processes are also included to derive meaningful insights and Analytics from datasets.

Predictive Maintenance tools and software

Predictive analytics tools and software are used to monitor equipment with conventional and advanced techniques, which allow the prevention of machine failures by planning maintenance in advance. These two types of techniques rely on numerous testing and supervising tools for tasks such as electrical insulation, vibration monitoring, temperature monitoring, leak detection, oil analysis, and so on. The use of Predictive Maintenance for condition monitoring to evaluate the performance of equipment in real-time is already widespread in many European countries. Advanced techniques are used in developed countries such as the USA; however, this is much less common in the Asia-Pacific and the Middle East. The advanced Predictive Maintenance process uses the Internet of Things as the core element; this allows different assets and systems to share, analyze, and act on the data. While IoT sensors capture information, Machine Learning then analyzes it and identifies areas that need urgent maintenance.

Predictive Maintenance with Machine Learning

When it comes to Predictive Maintenance with Machine Learning, we mostly imply automated Anomaly Detection. When the data generated by IoT sensors is monitored over time or in real-time, Machine Learning models use it to learn the metric stream’s normal behavior. The next step is to automatically identify anomaly data and events, find correlations, and make precautionary recommendations — which ultimately saves a lot of cost and time. The great thing about Machine Learning is that it can dynamically adjust to new data and understand what happens in real-time, also detecting and alerting staff of serious issues. You don’t need the manual configuration, data selection, or threshold settings that other maintenance measures demand.

Any Machine Learning-based approach demands relevant, sufficient, and quality data to build effective models that will provide higher accuracy in predictions. But if you have this three-pronged approach, you are ready to go.

Before a Predictive Maintenance solution is developed, the following factors should be addressed:

• Error history
• Maintenance/repair history
• Machine operating conditions
• Equipment metadata

Error history

When training a model, the algorithm should be fitted data on normal operational patterns as well as on failure patterns. That is why the training dataset should include enough training examples on normal as well as error samples. Maintenance records for the replacement of parts is a source to collect the necessary error events.

Repair/maintenance history

The maintenance history contains information on what repairs were made, what parts were replaced, etc. The presence of this information in the dataset is very critical; if it is absent, you could obtain misleading model results. The failure history is also represented by special error codes and parts order dates. Experts will help investigate the additional data, influencing the failure patterns.

Machine operating conditions

Streaming data of the equipment in operation that is sensor-based is important as a source of valuable dataset samples. The main assumption of Predictive Maintenance is that the condition of a machine gets worse over time as it performs its daily operations. The data is likely to have features that capture this aging pattern along with the anomalies that lead to degradation.

Static feature data

Static feature data implies the technical information of the equipment such as the date on which the equipment was made, the model, the start date of service, and the location of the system.

IoT-based Predictive Maintenance

IoT-based Predictive Maintenance competes with the time-based approach. Some say that an IoT-based solution is a better choice, because mechanism failures are often linked to random reasons (80%) instead of its age (20%). There is a classic program for maintenance services, SCADA, but it allows only local implementation — whereas IoT permits storing as much as terabytes of data and the running of Machine Learning algorithms on several computers at a time.

The data on the parameters taken by the sensors the device or equipment is connected to and goes through many transitions. This is necessary to achieve the final target – a Predictive Maintenance application that will alert users to potential device and equipment failures. Let’s take a closer look at what these transitions are:

The device or equipment with sensors

During this step, we will identify the key values of the equipment we want to monitor (such as temperature and voltage for a battery) and set sensors to capture them.

Field Gateway

Data captured by sensors cannot go directly to the Cloud Gateway, so one more physical device is added to this sequence – a Field Gateway that filters and processes the data.

Cloud Gateway

The Cloud Gateway receives information from the Field Gateway and allows secure transmission and connectivity with different protocols of field gateways.

Data Lake

The next step is the Data Lake, which speaks for itself. The data gathered by sensors arrives “raw” and thus still contains irrelevant or inaccurate items. It is represented by sets of sensor readings measured at a certain time. When there is a need to have insights from the data stored here, it moves to the Big Data Warehouse.

Data Warehouse

In this step, the data is cleaned and structured, so it contains the parameters taken by the sensors along with time and contextual information on types, locations, and dates on which the parameters were taken. It is now ready to be fitted into the Machine Learning model.

Machine Learning model

In the Machine Learning step, we can reveal the hidden dataset correlations, detect abnormal data patterns, and predict future failures.

Web/Mobile Application

Finally, we can receive notifications and monitor alerts on potential needs in maintenance with a User Application.

IBM Predictive Maintenance service

IBM has a Predictive Maintenance service, which is called IBM Predictive Maintenance and Quality. Here, you can supervise, analyze, and report on the parameters collected from devices, and the system then gives you a recommendation for actions to take. This IBM solution is used for purposes such as predicting the downtime of an asset, drill maintenance logs to identify the most efficient repair measures and maintenance cycles, and investigating the root causes of asset failure faster to preemptively perform preventive procedures.

Predictive Maintenance companies

In addition to IBM, other large Internet corporations such as Azure, and AWS also claim to be Predictive Maintenance companies as long as they have this option included in their numerous Cloud solutions. They all suggest you run your dataset through their existing models to detect potential equipment failures and learn the recommended actions to prevent them. For instance, AWS has its Machine Learning application that works on other AWS services such as Sage Maker and Amazon S3 bucket.

Along with the Internet giants, SPD Technology is an Artificial Intelligence supporting partner. The company’s Machine R&D team knows the nuts and bolts of Machine Learning for Predictive Maintenance. SPD Technology offers custom software development services, and mobile app development services, as well as develops custom Predictive Maintenance solutions to precisely address each client’s problem and find the best Artificial Intelligence-based outcome.

How does Predictive Maintenance work?

Predictive Maintenance does not demand anything except informal mathematical computations to know when a machine needs repairing or replacement; this allows the performance of maintenance in a timely and effective manner. Also, with the help of Machine Learning, facility managers will gain more time to focus on necessary tasks instead of performing guesswork.

Traditionally, facility managers performed predictive maintenance work with the help of SCADA – a computer system used for gathering and analyzing real-time data. But this approach demanded manually coded thresholds, alert rules, and regulations. It did not take into account the dynamic behavioral patterns of the equipment or contextual data concerning the manufacturing process in general.

Instead, if Predictive Maintenance is built on Machine Learning algorithms, they are fitted with data such as information technology, operation technology, and manufacturing process information about the rate of production flow and how synchronous machines are with each other.

What are the 6 advantages/benefits of Predictive Maintenance?

We have already talked about what Predictive Maintenance is and what problems it solves, now let’s look at the advantages it can offer to its user:

• Maintenance costs are reduced by approximately 50%
• Unexpected failures are decreased by 55%
• Overhaul and repair time is 60% lower
• Spare parts inventory is cut by 30%
• Machinery Mean time between failures is increased by 30%
• Uptime is increased by 30%

While these may seem like magical improvements at first glance, remember these are alleged numbers from researchers give; in reality, the percentages are lower, as every case is unique. However, even if the percentages are cut in half, the evidence of improvement will be obvious for the business. Also, significant money will be saved if previous maintenance costs were huge. For example, an ordinary manufacturing business will save up to 10% on maintenance costs —which is the same as if it increases its benefit by 40%.

Why is Predictive Maintenance important?

The main reason why Predictive Maintenance is important is that it guarantees you will not have to run maintenance too soon and waste money on work that is not needed, or predict if it is too late to do anything because of excessive deterioration that has already happened.

As we witness the rising availability of companies to install sensor devices and associated technology at a lower cost on all machinery and equipment being used, streamlining the live data of a machine’s state to a supervising application is now a common practice. Predictive analytics software for the equipment supervision platform can help to prepare maintenance and schedule repairs to keep equipment in good condition. This increases OEE (overall equipment effectiveness) which implies higher equipment availability along with its quality and performance.

4 most common Predictive Maintenance uses

By analyzing large amounts of data, companies can detect signs of a probable failure or error that can harm their business or even some small processes in it.

Manufacturing and Internet of Things

GEMÜ, a well-known automation components and valve manufacturer, created a type of technology based on the Internet of Things (IoT) to supervise the performance of manufacturing processes to timely detect and eliminate deteriorating parts before they malfunction, which increases the efficiency of manufacturing processes.

Automotive

Today we have connected cars that produce a massive amount of data from sensors set in the vehicle. The collected data then goes directly to car manufacturers and dealerships, who then warn drivers about any issues and prevent them before the car breaks down.

Utility suppliers

Utility suppliers use Predictive Maintenance for better regulated internal work, using huge data amounts generated by smart meters to detect early traits of supply and demand issues and introduce measures to prevent outages.

Insurance

The Insurance industry can benefit from Predictive Maintenance while making more accurate Predictive Analytics on disastrous weather conditions.

Conclusion

The usage of Predictive Maintenance is not limited only to the Manufacturing and Automotive industries, but is mostly applied to these two. Not only does this technology reduce maintenance costs, but also it decreases unexpected failures, overhaul, and repair time by almost 60% as well as significantly increases equipment and device uptime. Manufacturing leaders are already beginning to understand the importance of using Predictive Maintenance with Machine Learning for the monitoring of expensive and complex machines; thus, industry 4.0 will rely on it. You could be among the pioneers in your market if you consider the use of this technology!