Implementing a predictive maintenance strategy: Complete guide

A predictive maintenance, strategy is a maintenance technique that uses advanced analytics to anticipate equipment failures. Facilities implementing predictive maintnenance, or PdM, can see annual savings such as $30,000 on replacement parts, $230,000 in scrap per line, or, overall, $500,000 in prevented maintenance costs. 

Implementing a predictive maintenance strategy successfully requires treating it as an organizational capability-building initiative rather than a technology deployment project. 

Advanced sensors, analytics platforms, and machine learning algorithms enable powerful insights. However, sustainable competitive advantage comes from: 

• Strategically selecting assets and monitoring technology
• Gathering and using data comprehensively
• Translating technical information into actionable decisions
• Developing internal expertise to improve and scale

Many facilities struggle to convert Industry 4.0 and IIoT concepts into tangible business results. They often perceive the solutions as overly complex. Or, they lack a strategic framework to identify and build upon success. 

This guide provides the strategic insights you need to build lasting capabilities rather than temporary solutions.

Our philosophy for implementing PdM focuses on developing your organization's ability to own, manage, and optimize a predictive maintenance program.

First, we’ll explain the fundamental concepts that drive a successful, data-driven implementation. Then we'll apply predictive maintenance techniques in a detailed, step-by-step framework. 

Understanding the "why" and the "how" of a predictive maintenance program helps you build skills that lead to lasting value and a competitive edge.

Phase One: Determining if predictive maintenance is a good fit

Implementing a predictive maintenance strategy starts with whether your organization can benefit from it. Facilities prepared for Industry 4.0 are more likely to succeed. But, they also need strong motivation to solve specific problems.

IIoT Readiness

Industry 4.0 readiness means having forward-thinking philosophies, continuous improvement structures, and innovation-focused cultures for a predictive model. Considerations include:

• Cloud readiness: If your organization is willing to adopt cloud-based solutions and has the IT infrastructure to do so.
• Data security: Understanding data security requirements, specific compliance standards, and the sensitivity of data in the cloud. 
• Integration: The ability to seamlessly exchange data and integrate with existing systems.

Readiness without motivation doesn’t guarantee success. Many times, it creates implementations designed to demonstrate progress to auditors rather than solve real problems. 

For example:

A major automotive manufacturer implemented PdM specifically to show annual review progress. But, the predictive maintenance program didn’t yield significant results because they chose a model machine that didn’t handle much work. There was virtually no impact or risk associated with the few times it broke down. 

Motivation

The right motivation achieves the ROI needed to justify scaling a PdM strategy. The “Five Why’s” method of root cause analysis helps uncover the true business impact. Factors to consider include: 

• Impact: How do current maintenance issues affect machine availability and product quality? 
• Risk: What could happen if this project doesn’t launch? 
• Potential: If the project is successful, what are the plans to scale and expand it? 
• Budget: Is there an established budget, and how are budgets developed for projects like this? 

For example:

A technology company allocated $750,000 for their initial predictive maintenance strategy. They didn't need to prove that vibration sensors work. They already used sensors extensively throughout their operations and understood the technology. 

Instead, they wanted to cast a wide net across multiple asset types and locations to establish a comprehensive baseline for their predictive maintenance program. This scale allowed us to target both critical assets and frequent-failure equipment simultaneously.

Phase Two: Strategic asset selection

Choosing the right asset when implementing predictive maintenance proof-of-concept work is critical for showing ROI and building scalable maintenance workflow

Five reasons not to choose a critical asset for a new predictive maintenance project

Critical assets usually receive priority for predictive maintenance proof-of-concept projects. But, a thorough business impact review often reveals much better candidates. Here’s why: 

1. Frequency provides more opportunity than criticality. A machine whose failure shuts down entire production sections but rarely fails won’t demonstrate PdM value over a 6-12 month assessment period.
2. Smaller machines provide more learning experiences. Monitoring many frequently-failing assets can yield dozens of opportunities for a maintenance team to refine their procedures. 
3. Multiple successes create stakeholder buy-in.  The cumulative effect of many successful predictions builds more credibility than a single critical asset intervention.
4. Less expensive repairs are easier to complete. Maintenance teams may hesitate to take action on predictive alerts for high-ticket items, but will do so faster with smaller repairs and replacements. 
5. Multiple repairs shows resource optimization. A core value proposition for predictive maintenance strategy is directing limited maintenance time to equipment that actually needs attention. 

The best proof-of-concept applications for implementing predictive maintenance are often the assets that maintenance teams initially dismiss as unimportant. 

For example:

An automated warehouse considered an air compressor for a PdM proof-of-concept because it can shut down a whole production line if it breaks. However, these machines are expensive to replace and only breaks down every 6 to 10 years. 

Meanwhile, the facility had hundreds of small, 2.5-horsepower conveyor motors. They had ten spares in stock and could replace them easily. That made them perfect for validation: 

• With 100 motors running constantly and known failure patterns, we could monitor 10-20 of them and demonstrate real value quickly. 
• They could focus only on specific units showing elevated readings during their short, 1-2 hour daily maintenance windows at night, instead of checking 20 motors on a schedule. 

Managing different stakeholder dynamics

This planning phase involves balancing the expectations of different stakeholders across organizational levels: 

• High-level stakeholders need demonstrated cost-effectiveness and clear ROI.
• Maintenance teams need actionable insights that improve daily operations. 

Proof-of-concept strategies are essential for building confidence and demonstrating value before scaling to enterprise-wide deployments

The assets most suitable for predictive maintenance offer genuine business impact with practical implementation feasibility.

While building confidence during a pilot phase, smaller repairs are easier and faster to execute than more expensive or labor-intensive work orders. The program can include more complex or costly machines and actions as it expands. 

Establishing metrics and ROI 

Most organizations require 100% ROI within six to twelve months. Choosing the right metrics and Key Performance Indicators (KPIs) to show the results and efficacy of a predictive maintenance strategy is critical.

Choose (KPIs) on your current maintenance maturity level:

• Run-to-falure: Measure reduction in emergency repairs and downtime
• Time-based maintenance: Measure efficiency gains and resource optimization
• Condition monitoring: Measure prediction accuracy and intervention timing
• Advanced predictive: Measure comprehensive optimization and cost reduction

This framework is a starting point. You can choose any of these KPIs if they’re appropriate for your operations. 

Soft metrics also help show business impact:

• Document practical, observable improvements that maintenance teams can directly connect to their daily operations.
• Understand and present cascading effects beyond maintenance, such as improving product quality or saving money by meeting more contractual production deadlines.

For example:

A semiconductor manufacturing company showed the true value of a PdM product by understanding the full ramifications of vacuum pump failures. The immediate problem was batch losses. But, those losses affected global supply chains with disruptions cascading through entire industries. 

This deeper understanding drove genuine motivation and justified investment in predictive maintenance capabilities.

Phase Three: Failure mode analysis and choosing technologies

With assets in place, the next step to implementing predictive maintenance techniques is determining: 

• What to measure 
• Which sensors to use to collect data. 

Essential predictive maintenance technologies

There are many different technologies and metrics available. Your facility likely does not need all of them.

 

• Vibration analysis is best for rotating equipment. It detects mechanical degradation in rotating equipment through continuous monitoring of bearing condition, alignment issues, and lubrication problems. This technique provides the earliest and most cost-effective indication of developing mechanical failures.
• Ultrasonic acoustic monitoring accomplishes similar objectives to vibration analysis through contact-free measurement techniques. However, integration complexity and environmental interference often make vibration analysis more practical for most industrial applications.
• Temperature monitoring detects temperature anomalies indicating electrical faults, mechanical friction, and thermal inefficiencies. When integrated with vibration analysis, temperature monitoring provides comprehensive asset health assessment for rotating machinery.
• Infrared thermography detects temperature anomalies. It accomplishes similar objectives to temperature monitoring through contact-free measuring. It’s particularly useful for applications where permanently mounting a sensor is unfeasible. However, it usually requires handheld instruments implemented manually. This method is more labor-intensive and provides intermittent, rather than real-time, monitoring.
• Oil analysis is suitable for slow rotating equipment. It provides detailed insights into lubricant condition, contamination levels, and wear particle composition. This technique excels in applications where lubricant degradation drives equipment failure modes.

Choose sensors based on actual failure patterns and environmental operating conditions rather than attempting comprehensive technology implementation.

For example: 

• Most rotating assets monitored for industrial applications experience mechanical failures such as bearing degradation, shaft misalignment, and lubrication problems. 
• Vibration analysis is best here because these mechanical issues manifest as the most consistent and early indicators at the optimal price point.
• Temperature monitoring integrates naturally with vibration analysis, providing complementary asset health insights. 
• However, temperature typically indicates higher severity conditions that may require immediate action. 
• Vibration analysis can detect developing problems over extended timeframes, enabling proactive maintenance scheduling.

Environmental conditions should also drive technology selection beyond basic failure mode analysis. 

For example: 

• Dry environments where electrical issues represent less than 1% of failures rarely justify investing in current transducers.
• Food and beverage facilities using clean-in-place processes. The risk of water ingress risks justifying current monitoring alongside vibration analysis. 

Phase 4: Data and analytics setup

Implementing predictive maintenance requires real-time data from sensors to create baseline data and identify patterns. Machine learning algorithms enable failure prediction by analyzing patterns in real-time data, while IoT sensors enable continuous data exchange between equipment and analytics platforms.

In manual applications, an analyst or someone on the maintenance team creates a work order based on the information. The analyst may be in-house or part of a third-party solution. 

In fully-automated applications, the system performs the analysis and issues a work order through computerized maintenance management system (CMMS) software. 

This architecture requires robust integration between monitoring platforms and existing maintenance management systems. The three main considerations here are: 

• Data ownership vs. outsourcing
• Real-time data vs. intermittent monitoring
• Technology integration and data architecture

Data ownership vs. outsourcing

Deciding whether to build that infrastructure and manage that data in-house or rely on a third-party depends on your current labor force and scaling capabilities.

Third-party predictive maintenance providers install their own wireless equipment on machines, track data, and provide recommendations. 

This approach is attractive to facilities that:

• Don’t have the technical expertise in-house to analyze data
• Have a small maintenance team that can’t also take on new work yet
• Cannot afford the expense or downtime required to purchase and install equipment

However, outsourcing PdM limits growth potential: 

• The high costs per facility make scaling prohibitive
• The consultant owns the data, preventing you from building machine learning capabilities in the future
• You don’t learn how to create new procedures and strategies when only responding to work orders

Building internal capabilities rather than outsourcing analytics creates long-term competitive advantages while reducing ongoing operational costs. 

It may require higher upfront costs and resource commitments. But, it enables comprehensive equipment optimization strategies that external service providers cannot deliver. 

For example:

A major food manufacturers had entire international reliability teams with dedicated engineers and analysts, yet they outsourced their condition monitoring to wireless providers who own all their data and keep it siloed. 

When we examined this arrangement, they realized they’re essentially paying twice for the same capability: The outside company was doing work their internal team could handle. 

Emerging AI and machine learning capabilities make managing analysis and decisions easier despite varying levels of in-house expertise. 

For example:

A manufacturing company was paying $5,000 per month to a wireless monitoring service. The service analyzed their vibration data and sent back recommendations like "clean, inspect, and lubricate." 

When we showed them that these recommendations follow basic decision trees that we could automate, they realized they were paying premium prices for what amounts to a simple "if this, then that" rule engine that any competent system can handle internally.

Real-time data vs. intermittent monitoring

Deciding between owning or outsourcing data also affects whether you collect real-time data or use intermittent monitoring. 

Virtually all third-party solutions use wireless sensors. Since they rely on batteries, they can only send data once every day or every few hours. 

Investing in sensors opens the possibility for continuous, real-time data streaming.

Continuous monitoring is more expensive to purchase and install than intermittent, wireless technology. But, it adds much more potential for accurate and actionable readings:

• Real-time fault detection with immediate operator notification
• High-resolution time waveform analysis for precise diagnostics
• Integration with process control for automatic shutdowns when necessary
• Complete data history for machine learning algorithm development

For example: 

We made our first predictive maintenance vibration sensor in 1994 to monitor machine condition, but we could never really tell customers what specifically to fix. Even sophisticated companies remained stuck in the condition monitoring phase, hiring PhDs to analyze data and make recommendations. 

Now, because we can analyze big data at low cost and using machine learning, we can finally move from, "Something is wrong" to, "Go fix your bearing." That's the breakthrough that's happening right now.

Receiving data points every few hours, or daily, does not establish as clear of a baseline as real-time data. Projects with low budgets or smaller maintenance and data analyst teams may find intermittent monitoring sufficient for:

• A proof-of-concept predictive maintenance program, or 
• Moving from a run-to-failure operation. 

However, scaling will involve starting over with wired sensors for maximum ROI. 

Consider a comprehensive wired setup for more actionable results and faster scalability when implementing predictive maintenance techniques if you have a larger facility or more advanced maintenance and data analysis teams.

Technology integration and data architecture

If you own your own data, then the next step is deciding whether to:

• Force predictive maintenance data through existing control systems, or 
• Implement dedicated pathways designed specifically for asset health monitoring.

Usually, sensor data routes through Programmable Logic Controllers (PLCs) and existing Supervisory Control and Data Acquisition (SCADA) systems. However, PdM data serves a fundamentally different purpose than process control data. 

Sending the data only through the control system creates significant challenges for predictive maintenance applications:

• Resource intensity: Controls engineers must program all data handling, requiring specialized expertise and time
• IT complexity: Data must be extracted from PLCs and published to analytics systems, involving IT teams to create dashboards, topologies, and thresholds
• Performance limitations: PLCs cannot handle the massive data streams required for true predictive analytics

For example: 

Many organizations route sensor data through expensive PLCs unnecessarily. They’re paying premium prices to use control system software as “data pumps” when direct sensor integration provides the same data more efficiently.

A parallel data stream provides simplified integration architecture. It allows for both machine control and comprehensive asset health monitoring and analysis. 

It provides a second route for data from machines and sensors. If you’re using IO-Link equipment from ifm, the data reaches the master and splits along the “Y-Path”:

• One stream sends values to PLC’s, SCADA’s, etc. 
• The other remains a digital signal that reaches an IIoT platform for PdM applications. 

The benefits of a parallel data stream like the Y-Path are:

• Eliminating programming requirements: Plug-and-work systems automatically configure sensors and create dashboards without custom programming
• Enabling rapid deployment: What traditionally takes weeks or months of engineering can be accomplished in hours or days

Phase 5: Deployment 

Beyond installing and connecting hardware and software, implementing predictive maintenance strategy involves building your organizational capability to use the alerts and diagnostic information efficiently. 

Establish clear maintenance protocols and training

Maintenance teams require clear escalation procedures and response protocols for different alert severities. Maintenance workflows should specify required tools, parts availability, and estimated repair durations for each alert type:

• Warning-level notifications allow scheduled maintenance during planned downtime windows.
• Alarm conditions may require immediate response to prevent equipment damage. 

Cross-functional collaboration between maintenance, operations, and IT teams ensures sustainable program operation and continuous improvement.

Skilled workforce development requires both technical training and change management support. Implementation success depends on maintenance teams understanding new tools while adapting to condition-based rather than time-based maintenance approaches.

Continuous improvement through data ownership

Real-time data collection creates opportunities for comprehensive equipment optimization that extend beyond basic condition monitoring. You can integrate predictive insights with process variables, production schedules, and quality metrics when you control sensor data. 

This approach enables predictive maintenance systems to evolve from simple fault detection toward: 

• Comprehensive process optimization 
• Quality improvement 
• Energy efficiency enhancement. 

Training programs and capability development

Training programs equip employees with skills through progressive responsibility transfer rather than comprehensive upfront education. 

The "I do, we do, you do" methodology builds sustainable capabilities.

Initial deployments require expert guidance and gradually build internal competency for independent installations. This approach recognizes that predictive maintenance strategy expertise develops through practical experience rather than theoretical training alone.

Predictive maintenance strategy: Best practices

Implementing a predictive maintenance strategy is fundamentally a learning process that evolves through practical experience. While strategic planning provides direction, success depends on mastering the operational details.

These best practices represent lessons learned from real-world deployments. They help you bridge the gap between well-designed proof-of-concept projects and sustainable, scalable programs:

• Sensor installation and data quality
• Environmental compensation and analysis
• Data translation and team communication
• Maintenance frequency optimization

Consider these basic best practices as your starting framework for building expertise that develops through hands-on application.

Sensor installation and data quality

Sensors monitor equipment performance continuously and return high-quality data when properly installed:

• Sensors require strategic placement: One sensor for equipment under two feet, sensors at both ends for larger equipment. Drill and tap installation provides optimal accuracy, Loctite adhesives work when permanent mounting isn't feasible. Avoid magnetic mounting for continuous monitoring.
• Mounting methodology directly impacts data accuracy and equipment reliability. 
  • Drill and tap installation provides optimal rigid coupling between sensors and monitored equipment. 
  • Loctite adhesives offer the next-best alternative when permanent mounting isn't feasible. 
  • Magnetic mounting serves temporary applications but compromises long-term measurement consistency, making it unsuitable for continuous monitoring programs.
• Avoid mounting sensors on equipment fins, casings, or shrouds. These flexible surfaces amplify sensor housing vibration rather than actual equipment conditions. They create a trampoline effect, leading to false alerts and reduced diagnostic accuracy.

Environmental compensation and analysis

Monitoring systems should calculate delta values between asset readings and ambient conditions rather than using absolute readings. This compensation prevents false alarms during environmental variations while maintaining sensitivity to actual equipment degradation.

Real-time data enables immediate response when properly contextualized for environmental conditions. 

Smart analytics automatically adjust thresholds based on environmental factors, ensuring alerts indicate genuine equipment issues.

For example:

During heat waves, absolute temperature readings in automotive plants in southern climates suggest problems. But, equipment is operating normally relative to high ambient conditions.

Data translation and team communication

Transform technical sensor outputs into actionable language that reflects required maintenance activities. This translation approach eliminates the complexity barrier that can prevent you from successfully implementing predictive maintenance techniques. 

Machine learning models improve over time, but maintenance teams need immediate understanding of current conditions. 

For example:

Our vibration sensors output values like ARMS, VRMS, peak acceleration, and crest factor. These terms don’t resonate at all with most maintenance managers. 

In our system, we rename these parameters to more intuitive descriptions. ARMS becomes "friction" or "lubrication."

When maintenance gets an alert, they don't see confusing technical jargon. Instead, they see that the "lubrication" parameter is elevated, which tells them to send someone out to clean, inspect, and lubricate the equipment.

Maintenance frequency optimization

Establish procedures where your maintenance team can address alarm condition alerts and work orders as soon as possible. This is different from predetermined maintenance schedules or run-to-failure scenarios. 

Part of the predictive maintenance strategy is eliminating unnecessary interventions while ensuring critical maintenance occurs before failures develop. Equipment reliability improves when maintenance frequency responds to actual equipment condition rather than arbitrary time intervals. 

Sensor installation and data quality

Sensors monitor equipment performance continuously and return high-quality data when properly installed:

• Sensors require strategic placement: One sensor for equipment under two feet, sensors at both ends for larger equipment. Drill and tap installation provides optimal accuracy, Loctite adhesives work when permanent mounting isn't feasible. Avoid magnetic mounting for continuous monitoring.
• Mounting methodology directly impacts data accuracy and equipment reliability. 
  • Drill and tap installation provides optimal rigid coupling between sensors and monitored equipment. 
  • Loctite adhesives offer the next-best alternative when permanent mounting isn't feasible. 
  • Magnetic mounting serves temporary applications but compromises long-term measurement consistency, making it unsuitable for continuous monitoring programs.
• Avoid mounting sensors on equipment fins, casings, or shrouds. These flexible surfaces amplify sensor housing vibration rather than actual equipment conditions. They create a trampoline effect, leading to false alerts and reduced diagnostic accuracy.

Environmental compensation and analysis

Monitoring systems should calculate delta values between asset readings and ambient conditions rather than using absolute readings. This compensation prevents false alarms during environmental variations while maintaining sensitivity to actual equipment degradation.

Real-time data enables immediate response when properly contextualized for environmental conditions. 

Smart analytics automatically adjust thresholds based on environmental factors, ensuring alerts indicate genuine equipment issues.

For example:

During heat waves, absolute temperature readings in automotive plants in southern climates suggest problems. But, equipment is operating normally relative to high ambient conditions.

Data translation and team communication

Transform technical sensor outputs into actionable language that reflects required maintenance activities. This translation approach eliminates the complexity barrier that can prevent you from successfully implementing predictive maintenance techniques. 

Machine learning models improve over time, but maintenance teams need immediate understanding of current conditions. 

For example:

Our vibration sensors output values like ARMS, VRMS, peak acceleration, and crest factor. These terms don’t resonate at all with most maintenance managers. 

In our system, we rename these parameters to more intuitive descriptions. ARMS becomes "friction" or "lubrication."

When maintenance gets an alert, they don't see confusing technical jargon. Instead, they see that the "lubrication" parameter is elevated, which tells them to send someone out to clean, inspect, and lubricate the equipment.

Maintenance frequency optimization

Establish procedures where your maintenance team can address alarm condition alerts and work orders as soon as possible. This is different from predetermined maintenance schedules or run-to-failure scenarios. 

Part of the predictive maintenance strategy is eliminating unnecessary interventions while ensuring critical maintenance occurs before failures develop. Equipment reliability improves when maintenance frequency responds to actual equipment condition rather than arbitrary time intervals. 

Conclusion

The path to successfully implementing predictive maintenance techniques lies in developing organizational capabilities, not just installing monitoring equipment. Invest time and resources in a program that: 

• Meets your organization’s specific needs 
• Can be deployed by your current labor force
• Is scalable to more machines and locations

Throughout the process, focus on building internal expertise. Invest in training and resources that enable your teams to manage PdM operations. 

Ultimately, sustainable competitive advantage comes from what your organization learns, not what it purchases.

How to get started

The best predictive maintenance strategy is simple to implement and maintain. It also prepares a facility for the future. The best way to create a customized preventative maintenence program is to start with a proof-of-concept project. For a free consultation with a Solutions Architect, fill out the form below. 

For immediate assistance, contact our service center: 
info.us@ifm.com
800-441-8246