What is Predictive Maintenance?

Predictive maintenance is a strategy that organizations use to estimate and plan their operational equipment's maintenance schedules. 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 can 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 the temperature or pressure goes beyond a set threshold or if machine usage is higher than you expect. Predictive maintenance anticipates potential machinery damage and schedules maintenance checks before that damage happens. Your organization can use predictive maintenance to maximize production time by increasing asset uptime and reliability.

Predictive maintenance is important because physical machinery can be damaged. Components can fail or degrade, and performance can slow or vary beyond expected operational limits. Physical equipment failure and degradation are 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

Sometimes, equipment engineering design and the arrival of new technologies also reduce equipment lifespan. This can 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.

Predictive maintenance involves monitoring, analysis, and action based upon gathered insights.

Monitoring

You monitor equipment throughout its use with a diverse range of IoT sensors available for this very purpose. Sensors measure conditions such as 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. 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.

Analysis

You 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. You can build predictive models by analyzing historical data and correlating it with known failures. Turning raw sensor readings into usable information requires strong computational capabilities.

Action

The predictive models estimate when a piece of equipment is likely to fail based on current and past data patterns. The system can then be designed to create proactive maintenance schedules based on its future analysis. It can use outputs such as 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 the type of analysis you perform. Customizing predictive maintenance solutions requires engineers, infrastructure architects, and data scientists to create the right system, configuration, and feedback loops for improvement.

Modern predictive maintenance technologies combine connected IoT devices, advanced analytics, and data processing intelligence platforms to support your predictive maintenance strategy. These tools gather real-time data and process that data, giving you direct insight into the internal functioning of your systems.

Sensors enable continuous condition monitoring, capturing equipment data such as temperature, pressure, and performance. Depending on the equipment, sensors may even use techniques such as vibration analysis or oil analysis to detect early signs of degradation, which allows for early fault detection before any major problems arise.

Sensors transmit their data to centralized monitoring platforms, where AI and machine learning models use predictive analytics to process all the collected information and identify patterns. For example, high readings for vibration on a typically stationary tool could suggest equipment failure. These tools review historical trends and the maintenance history of a machine, looking to predict equipment failures and anticipate these risks.

Finally, specialized predictive maintenance software and other predictive maintenance tools transform these data insights into specific actions that you should take. For example, they may produce a list of maintenance tasks for you to follow. Alternatively, they may directly link to other systems like inventory management, automatically checking that you have spare parts available for any critical components that may be under strain.

All of these parts are individual components, but come together as a singular system infrastructure to help in implementing predictive maintenance successfully. Together, they promote improved equipment reliability, boost asset performance, and significantly reduce machine downtime.

Predictive maintenance programs can benefit your organization in several ways.

Reduce downtimes

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 to increase business continuity.

Reduce unnecessary maintenance costs

Preventive maintenance, on a timed or per-use schedule, 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 industrial IoT devices for detailed analytics of your entire business operations.

Predictive maintenance is typically used in cases with large, complex, physical systems. Here are some examples:

• Manufacturing plants and factories

• Operational technology in critical infrastructure

• 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.

Your organization can also adopt preventive maintenance and inspection-based maintenance practices.

Predictive maintenance versus 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 architected, configured, deployed, and maintained correctly.

Predictive maintenance versus 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.

Predictive maintenance versus condition-based maintenance (CBM)

Predictive maintenance (PDM) and condition-based maintenance both monitor equipment, but they differ in how the decision-making process post-analysis aligns with your maintenance strategy.

Condition-based maintenance uses ongoing condition monitoring to track performance indicators like pressure and vibration. If any of your predefined thresholds for these readings are exceeded, a maintenance job is automatically triggered. By doing this, you reduce unplanned downtime as you react to current conditions to mitigate future risk.

The predictive-based maintenance process uses real-time data, machine learning models, and historical trends to analyze data and predict potential failures before a machine reaches these threshold levels. It’s more about trying to anticipate equipment failures earlier and responding proactively.

Both condition-based maintenance and predictive maintenance minimize downtime, lower operating costs, and prevent many costly repairs. However, the maintenance strategy you employ depends on how much risk your business is willing to take on.

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 resources, 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 according to the maintenance strategy

• Ensuring sensors are working correctly

• Setting the right guardrails for maintenance alerting and maintenance team monitoring

• Performing the right equipment health data analytics for predictive maintenance

• Deciding when and if to use preventive maintenance solutions 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 schedules.

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 Managed Grafana offers scalable and secure data visualization for your operational metrics, logs, and traces.

• Amazon Rekognition offers pretrained and customizable computer vision (CV) capabilities to extract information and insights from your images and videos.

Amazon SageMaker allows you to build, train, and deploy custom ML models for predictive maintenance software with fully managed infrastructure, tools, and workflows.

Get started with predictive maintenance on AWS by creating a free account today.