What is predictive maintenance?
Predictive maintenance is the strategy that organizations use to estimate and plan their operational equipment's maintenance schedule. The strategy is designed to optimize equipment performance and lifespan. With Internet of Things (IoT) devices, your organization can use smart sensors to monitor every aspect of your machinery's performance. Predictive maintenance solutions integrate sensor data with business operational data and apply analytics based on artificial intelligence (AI) to derive meaning. You can use the derived meaning to predict the future state of the equipment and anticipate potential machinery issues before they arise. For example, you might expect issues if temperature or pressure go beyond a set threshold or machines usage is higher than you expect. Predictive maintenance anticipates potential machinery damage and schedules maintenance checks before the damage happens. Your organization can use predictive maintenance to maximize production time by increasing asset uptime and reliability.
Why is predictive maintenance important?
Predictive maintenance is important because physical machinery can be damaged. Components can fail or degrade, and performance can slow or become variable beyond expected operational limits. This physical equipment failure and degradation is due to a wide range of causes:
- External events and conditions
- Wear from regular use
- Excessive wear due to equipment use outside the bounds of the machinery's expected capacities or function
Overall equipment engineering design and new technology also reduce an equipment's lifetime. They also affect maintenance and replacement schedules.
When you integrate increasingly complex and different types of equipment in industrial machinery systems, any failure or degradation of one component negatively affects other components in the chain. This leads to unexpected results. Your organization can use predictive maintenance solutions to reduce the chance of equipment failure and avoid degradation outside of reasonable bounds.
How does predictive maintenance work?
Predictive maintenance involves monitoring, analysis, and action based upon gathered insights.
You have to monitor equipment throughout its use with a diverse range of IoT sensors available for this very purpose. Sensors measure temperature, vibration, humidity, and other parameters that provide insights into machine health.
For instance, a temperature sensor indicates whether a machine is becoming hotter after extended use. Or images from a camera could show if a valve is not opening as wide as it should be. Equipment is monitored continuously or at frequent intervals to ensure timely data capture and increase the chance of detecting anomalies.
You can analyze the data gathered from sensors to determine how quickly equipment is degrading or if it will soon fail. IoT devices communicate data to a centralized system. Here, machine learning (ML) and other advanced AI algorithms analyze the data to detect deviations from established baselines or patterns. They build predictive models by analyzing historical data and correlating it with known failures. Turning raw sensor readings into usable information requires strong computational capabilities.
The predictive models estimate when a piece of equipment is likely to fail based on current and past data patterns. The system creates proactive maintenance schedules based on its future analysis. It also uses emails, messages, dashboards, or other mechanisms to alert the maintenance team to potential upcoming failures or time-critical anomalies. As your organization performs maintenance and gathers more data over time, the predictive models become more accurate and reliable.
Predictive maintenance technology
Predictive maintenance work is a complex field with many moving parts. It requires systems that support the storage, transfer, and analysis of a massive amount of data. This is often real-time, streaming data that's combined from hundreds, if not thousands or millions, of different IoT sources. Data transfer requires dedicated networks, with storage in data lakes, and processing using dedicated high-performance server clusters.
The exact infrastructure you need to support a predictive maintenance program depends on the system and architecture you use. It also depends on your sensors, data types, and type of analysis you perform. Customizing predictive maintenance solutions requires engineers, infrastructure architects, and data scientists to create the right configuration.
What are the benefits of predictive maintenance?
Predictive maintenance programs can benefit your organization in several ways.
Waiting for equipment to fail before you fix it is known as reactive maintenance. Unplanned downtimes impact the entire operational schedule. In contrast, predictive maintenance decreases the risk of unexpected equipment failures. You can plan corrective maintenance in advance and shift downtimes to noncritical times. If you need to, you can also bring in backup equipment to use during repairs and increase business continuity.
Reduce unnecessary maintenance costs
Preventive maintenance usually keeps machinery in good operational condition. However, this maintenance is not always necessary from a degradation perspective. When you use a predictive maintenance program, you trigger maintenance following greater usage or time than you would expect under regular scheduling. This could be due to less equipment use or other factors. This process results in reduced maintenance costs for new parts and for resourcing of the maintenance team.
Increase integrated system understanding
By using a predictive maintenance program, you can develop a detailed, real-time view of the overall health of a complex system. In the past, this was not possible. Only timestamped inspection-of-defect reports were available to comprise a system overview. These days, you can integrate data across all your IoT devices for detailed analytics of your entire business operations.
What are the use cases of predictive maintenance?
Predictive maintenance is typically used in cases with large, complex, physical systems. Here are some examples:
- Manufacturing plants and factories
- Building and industrial facilities
- Transport and logistics
- Energy and utilities operations
- Mining operations
- Complex robotics
- Laboratory services
Companies that own or manage these systems, operations, or facilities can greatly benefit from using predictive maintenance. It'll also give them a competitive edge.
What's the difference between predictive maintenance and other types?
Your organization can also adopt preventive maintenance and inspection-based maintenance practices.
Predictive maintenance vs. preventive maintenance
With predictive maintenance, you predict a failure or degradation of equipment in advance, then perform maintenance preemptively before the event occurs.
Preventive maintenance, on the other hand, is when you perform maintenance on a set schedule. This maintenance schedule may be based on either time periods or measurable usage units (such as the number of revolutions of a fan). Typically, these maintenance schedules are specified by the manufacturer of the equipment.
You can simultaneously use both predictive and preventive maintenance techniques, or you can use one method over the other. Predictive maintenance is more advanced in nature than preventive maintenance. It's more effective when architectured, configured, deployed, and maintained correctly.
Predictive maintenance vs. inspection of defects
Inspection of defects is a process where you investigate the current state of equipment to decide whether to initiate new maintenance. It can also validate the need for proposed maintenance. Typically, this process involves on-the-ground analysis, such as physically photographing rust on a machine. It can also involve remote analysis techniques, such as vibration analysis or IoT-assisted capture.
You can use inspection of defects along with both predictive and preventive maintenance. An inspection can also be used for purposes other than maintenance scheduling. For example, inspection of defects may come into play if a company wants to sell its industrial facilities. The buyer may want to know the extent of any damage or wear to machinery, which would affect the cost of purchase.
What are the challenges in implementing predictive maintenance?
Predictive maintenance relies on a significant amount of investment in planning, IoT purchasing, operation, maintenance, analysis activities, and continuous improvement and management. The amount of time, human resourcing, and money needed for effective predictive maintenance is sometimes beyond the reach of smaller operations.
Before your organization deploys a predictive maintenance solution, consider the following challenges:
- Capturing the right data with the right sensors
- Capturing the right sensitivity level of data
- Ensuring sensors are working correctly
- Setting the right guardrails for maintenance alerting
- Performing the right analysis for predictive maintenance
- Deciding when and if to perform preventive maintenance and inspections for defects
- Integrating new equipment components into the predictive maintenance system
- Configuring automated computerized maintenance management systems based on analysis
Additionally, your organization also has to be aware of any legal, compliance, or insurance obligations regarding scheduled maintenance. This is most relevant if you plan to follow predictive maintenance schedules that are less frequent than vendor-recommended maintenance scheduling.
How can AWS help with your predictive maintenance requirements?
Amazon Web Services (AWS) provides a wide variety of services to help your organization develop and deploy predictive maintenance solutions. These services can operate on a massive scale without the challenges of investing in owned infrastructure and maintenance.
AWS IoT services and solutions help you collect and store sensor data for predictive maintenance. Here are some examples:
- AWS IoT Core lets you connect billions of IoT devices and route trillions of messages to AWS services without managing infrastructure
- AWS IoT Device Management helps you register, organize, monitor, and remotely manage IoT devices at scale
- AWS IoT Events monitors your equipment or device fleet for failures or changes in operation, then starts necessary actions
Machine Learning on AWS lists many fully managed services for analyzing your sensor data. Here are a few examples:
- Amazon Lookout for Equipment is an ML industrial equipment monitoring service that detects abnormal equipment behavior, so you can act and avoid unplanned downtime
- Amazon Monitron is an end-to-end system that uses ML to detect abnormal conditions in industrial equipment and enable predictive maintenance
- Amazon Rekognition offers pretrained and customizable computer vision (CV) capabilities to extract information and insights from your images and videos.
With Amazon SageMaker, you can build, train, and deploy custom ML models for predictive maintenance software with fully managed infrastructure, tools, and workflows. You can browse examples of Predictive Maintenance Using Machine Learning on the AWS Solutions Library to get started. Using our code on GitHub, with an example dataset of turbofan degradation, you can explore AWS predictive maintenance solutions in action. Customize with your own data to gain a deeper understanding of our capabilities for your unique use case.
Get started with predictive maintenance on AWS by creating an account today.