Predictive Maintenance for PLCs: 6 Solutions to Consider in 2026

Compare predictive maintenance approaches for PLC and industrial automation environments, from controller-level and edge analytics to cloud-based predictive maintenance and asset performance management.

Your motors, bearings, heaters, power supplies, and other critical components can provide condition information that helps reveal developing equipment problems. The challenge is turning that information into useful maintenance insight without creating an unnecessarily complicated automation and data architecture.

For manufacturers, the right predictive maintenance solution depends on more than the analytics. You also need to consider where data is collected, where analysis occurs, how the solution connects with the PLC and other automation systems, and how maintenance teams ultimately receive and act on the information.

This guide examines those considerations, explains OMRON's approach, and looks at five other solutions manufacturers may encounter when evaluating predictive maintenance technology.

Editorial disclosure: This article is published by OMRON Automation Americas. Product capabilities described here are based on publicly available information. Features, connectivity, hardware requirements, licensing, and deployment options can vary by product and configuration. Confirm requirements with each manufacturer for your application.

Predictive maintenance solutions covered in this guide

The six offerings represent different approaches to industrial maintenance and analytics:

1. OMRON Sysmac AI and condition-monitoring solutions: Controller-level and edge-oriented analytics integrated with industrial automation
2. Siemens Senseye Predictive Maintenance: Cloud-based predictive analytics
3. Fiix by Rockwell Automation + FactoryTalk Optix: CMMS workflows connected with operational data
4. Schneider Electric EcoStruxure Asset Performance Management: Asset health and risk-based maintenance planning
5. Mitsubishi Electric MELSOFT MaiLab: Manufacturing data analysis and AI-assisted diagnosis
6. ABB Ability Genix APM: Asset-performance management with predictive and prescriptive analytics

These products do not all solve the problem the same way. That distinction is important when comparing them.

What should manufacturers look for in predictive maintenance?

A predictive maintenance project should start with your equipment and automation architecture, not a software feature list.

Before selecting a solution, evaluate these areas.

PLC and automation connectivity

Where will the condition data come from?

Identify the controllers, sensors, drives, electrical components, and other equipment associated with your most important assets. Then establish how that information will reach the analytics environment.

The fewer unnecessary layers between the equipment, control architecture, analytics, and maintenance workflow, the easier the overall system may be to understand and maintain.

Edge versus cloud analytics

Predictive maintenance does not necessarily have to happen in the cloud.

Analysis can take place:

• in or close to the controller,
• on an industrial edge device,
• on another on-premises computing platform,
• in cloud infrastructure,
• or through a hybrid architecture.

The appropriate choice depends on the application.

Learn more about OMRON's approach to IIoT edge data and control.

Industrial communication requirements

Identify the protocols and interfaces already present in your plant and determine exactly what each proposed solution requires.

Depending on the architecture, that can include technologies such as:

• EtherNet/IP
• EtherCAT
• OPC UA
• MQTT
• Modbus TCP
• database connectivity

Protocol support varies by controller, communication unit, software, and configuration. Always validate compatibility for the specific products being connected rather than assuming portfolio-wide compatibility.

Condition-monitoring requirements

Start with the failure modes you're trying to detect.

Different assets can require different condition information. Your architecture should therefore be based on the equipment and maintenance problem rather than simply adding more sensors.

Maintenance workflow

Detecting an anomaly isn't the end goal.

Determine how the maintenance team will receive the information and what happens next. Depending on the architecture, useful information may need to reach an HMI, SCADA platform, maintenance application, historian, database, MES, or another plant system.

The OMRON approach: Bring intelligence closer to the machine

OMRON's approach is particularly relevant when manufacturers want condition monitoring and analytics closely connected with the automation architecture.

Rather than treating predictive maintenance exclusively as a separate enterprise or cloud application, OMRON offers technologies that can bring machine data, condition monitoring, industrial communications, and applicable analytics closer to the equipment.

At the center of this approach is the Sysmac automation platform, which provides an integrated automation environment for machine control.

For operations already using OMRON technology, or manufacturers considering a control-platform modernization, this creates an opportunity to think about maintenance information as part of the machine architecture itself.

Sysmac AI and machine-level anomaly detection

OMRON's AI Controller approach applies machine-learning capabilities within the automation environment.

Instead of relying entirely on an external analytics platform, machine information can be analyzed closer to the production process. This provides manufacturers another architectural choice when evaluating predictive maintenance.

This approach can be particularly attractive when:

• equipment information needs to remain close to the machine;
• local monitoring is important;
• the organization wants to minimize dependence on external connectivity for applicable functions;
• machine automation and condition analytics need to be closely coordinated.

The exact architecture and available functions depend on the controller and configuration selected.

Explore the OMRON Sysmac platform.

Build condition monitoring into the automation architecture

Successful predictive maintenance starts with relevant equipment data.

OMRON offers technologies for collecting condition information from manufacturing equipment and incorporating it into the automation environment.

IO-Link and intelligent sensing

OMRON IO-Link products provide a way to bring sensor and equipment information into the control architecture.

For predictive and condition-based maintenance applications, that can help build an information path from the physical asset to the automation system.

Instead of considering sensing, control, and maintenance analytics as isolated systems, manufacturers can evaluate them as parts of the same architecture.

Start with the right assets

Not every component needs the same level of monitoring.

A good starting point is identifying equipment where an unexpected failure materially affects the production process and where meaningful condition information is available.

That might include applications involving:

• rotating equipment,
• heaters,
• electrical components,
• or other critical machine systems.

The appropriate sensing and analytical approach should then be selected for the specific equipment and potential failure mode.

Explore OMRON predictive maintenance solutions.

Use edge data infrastructure to connect existing equipment

Not every predictive maintenance project starts with a new machine.

Manufacturers often need to obtain useful information from existing equipment and mixed automation environments. That creates a different challenge: making operational data accessible without unnecessarily redesigning the control system.

The OMRON DX100 provides an industrial edge data layer for collecting, transforming, storing, and routing data.

That makes the DX100 relevant to architectures where manufacturers want to make existing industrial data available to monitoring, visualization, analytics, or other information systems.

It is important to distinguish data infrastructure from predictive analytics: the DX100 can support the data architecture used by a predictive-maintenance application, while the appropriate condition-monitoring or analytics technology should be selected separately.

Why edge architecture matters for predictive maintenance

Edge and cloud architectures serve different needs.

Edge processing means relevant data is processed on or closer to the equipment rather than requiring every analytical operation to occur in remote cloud infrastructure.

That can be important in industrial automation for several reasons.

Local operation

Certain monitoring and analytics functions can operate within the local industrial architecture without depending entirely on an external cloud service.

Data architecture

Manufacturers can decide which equipment information should stay within the local architecture and which information should move to higher-level or cloud systems.

Integration with automation

When analytics are closer to the controller and equipment, the information architecture can remain more closely connected with the machine-control environment.

Cloud still has an important role

Cloud platforms can be valuable where manufacturers want centralized analytics across many assets, production lines, or facilities.

The question, therefore, isn't simply "edge or cloud?"

A better question is:

Which functions need to happen close to the machine, and which are better handled centrally?

For many manufacturers, the resulting answer may be a hybrid architecture.

Explore OMRON IIoT edge data and control.

Connecting predictive maintenance with the rest of the plant

Condition information becomes more useful when it can move through the systems that engineering and operations teams already use.

Depending on the selected OMRON controller, communication components, software, and overall architecture, data can be exchanged with other industrial and information systems.

When evaluating an architecture, determine what needs to connect with:

• PLCs and machine controllers
• HMI systems
• SCADA
• historians
• databases
• MES
• maintenance applications
• enterprise systems

The specific interfaces available depend on the product and configuration.

For implementation-specific connectivity questions, visit OMRON Support and Downloads or contact OMRON.

How five other industrial maintenance solutions compare

OMRON isn't the only approach manufacturers will encounter.

These alternatives illustrate why it is important to define the maintenance and architectural problem before comparing individual product features.

Siemens Senseye Predictive Maintenance

Consider when cloud-based predictive analytics across large asset populations or multiple sites are important.

Siemens Senseye Cloud Application is positioned as cloud-based predictive-maintenance software. Siemens describes the application as working with existing data from sources including legacy machines, historians, IoT platforms, databases, and sensors.

How the approaches differ: Manufacturers comparing Senseye with OMRON should determine whether the priority is centralized cloud-based predictive analytics across many assets or closer integration between condition analytics and individual machine-automation environments.

Fiix by Rockwell Automation + FactoryTalk Optix

Consider when maintenance-management workflows are the center of the application.

Fiix is a CMMS, while FactoryTalk Optix can provide connectivity between operational systems and maintenance workflows. Official Fiix information describes connections involving PLCs, sensors, SCADA, MES, DCS, historians, MQTT, and OPC UA sources.

How the approaches differ: This architecture is especially relevant when the desired outcome centers on connecting operational information to CMMS processes. OMRON's approach can instead place greater emphasis on condition information and analytics within or close to the machine automation architecture.

Schneider Electric EcoStruxure Asset Performance Management

Consider when asset health, criticality, risk, and maintenance prioritization are major requirements.

Current Schneider Electric EcoStruxure Asset Performance Management information describes use of technical, telemetry, inspection, and maintenance data and integration with systems including SCADA/ADMS, CMMS/EAM, and GIS.

How the approaches differ: This represents a broader asset-health and risk-management use case rather than a controller-centered approach to machine condition analytics.

Mitsubishi Electric MELSOFT MaiLab

Consider when manufacturing data analysis and AI-assisted diagnosis are the primary needs.

MELSOFT MaiLab provides manufacturing data analysis and diagnosis capabilities. Mitsubishi Electric describes functionality for creating AI models and using them for diagnosis.

How the approaches differ: When evaluating MaiLab against OMRON, manufacturers should consider where they want analytical models to be developed and executed and how closely the resulting analytics need to interact with the machine-control architecture.

ABB Ability Genix APM

Consider when enterprise asset-performance management is the primary objective.

ABB Ability Genix APM provides predictive and prescriptive analytics alongside condition monitoring, asset models, fault monitoring, recommendations, and maintenance-system integration. ABB also documents condition monitoring at the edge within the offering.

How the approaches differ: Genix APM addresses broader asset-performance-management requirements, while OMRON can be considered where manufacturers want condition information and applicable analytics more closely aligned with machine automation.

Comparing the six approaches

Rather than asking which solution has the longest feature list, evaluate where it fits in your architecture.

This framing avoids treating fundamentally different software and automation technologies as interchangeable products.

When should you consider OMRON for predictive maintenance?

OMRON deserves particular consideration when predictive maintenance needs to fit closely into the automation architecture rather than operate exclusively as a separate enterprise analytics application.

Three questions can help determine whether this approach fits your plant.

1. Are you modernizing the machine-control architecture?

If you're replacing legacy controls, designing new equipment, or standardizing automation, it can be useful to consider condition information at the same time rather than adding another system later.

Explore OMRON automation systems.

2. Do you want analytics closer to the machine?

Where local processing matters, controller and edge-oriented architectures provide an alternative to sending every analytical workload to a remote platform.

Explore the OMRON Sysmac platform.

3. Do you need a path from equipment data to broader plant systems?

Machine information may ultimately need to support multiple applications.

The appropriate OMRON architecture can bring together sensing, controls, edge data infrastructure, analytics, and connections to higher-level systems, depending on the selected components.

Explore:

• Predictive maintenance
• Sysmac platform
• IO-Link products
• DX100
• IIoT edge data and control
• Smart connected control panels

How to start your predictive maintenance strategy

Before choosing technology, document your existing environment.

1. Identify critical equipment

Prioritize equipment where unexpected downtime has a meaningful production or maintenance impact.

2. Identify the condition information available

Determine what sensors, controller data, electrical measurements, alarms, and historical information are already available.

3. Map the automation architecture

Document the PLC, network, HMI, SCADA, historian, database, MES, and maintenance systems associated with those assets.

4. Decide where analytics belong

Determine which functions need to happen close to the machine and which could be performed on another edge system, an on-premises platform, or in the cloud.

5. Validate connectivity

Check the exact protocols, communication modules, gateways, licenses, software, and integration work required for the proposed architecture.

6. Connect the insight to maintenance action

Define who receives an anomaly or condition alert, what information they receive, and how that insight fits into the maintenance process.

Bring predictive maintenance closer to your automation

Predictive maintenance doesn't have to begin with a separate analytics platform.

For manufacturers modernizing their automation architecture, OMRON provides a path for bringing sensing, condition information, industrial data, control, and applicable analytics closer together.

The result is an opportunity to design maintenance intelligence as part of the automation strategy rather than treating it exclusively as an additional software layer.

Ready to explore predictive maintenance for your equipment?

Talk to an OMRON automation expert about your PLC, machine, condition-monitoring, and industrial data requirements.

Or find an OMRON distributor.