What is predictive maintenance? Complete overview and insights

Predictive maintenance is a proactive maintenance strategy that prevents machine failures and optimizes maintenance schedules. It uses real-time data analytics from sensors to monitor machine health and detect signs of an impending breakdown. This information allows maintenance teams to schedule interventions before a problem results in equipment failure. 

Formal research, supported by field experience across hundreds of industrial deployments, reveals a fundamental truth: Predictive maintenance transforms production planning and efficiency to produce significant financial impact.

A 2021 study involving dozens of machinery maintenance managers across various industries supports this claim. Facilities using predictive or preventive maintenance experienced 87.3% fewer defects and 52.7% less downtime than those relying on older strategies.

These validated performance improvements deliver clear cost savings and a competitive advantage by improving throughput and quality control while conserving resources. They come at a critical time: 

• Consumer demand is increasing: Despite inflation concerns, annual consumer expenditures in 2023 rose 5.9% year-over-year, down only slightly from post-pandemic annual growth of over 9%.

• Labor shortages persist: Nearly 500,000 U.S. manufacturing jobs are unfilled in 2025.

More efficient production allows organizations that adopt predictive maintenance strategies to outperform those who haven’t closed that resource gap. 

A fundamental shift in operational philosophy

Traditional maintenance strategies rely on scheduled intervals or reactive fixes. Teams have limited visibility into asset health and rely on asset cost and criticality assessments to make decisions.

Successful PdM programs leverage real-time data analytics to initiate timely repairs before failures and unplanned downtime occur. 

Organizations shift from asking, "Is something wrong?" to definitively knowing, "Replace this bearing on Machine 3 within two weeks to avoid a breakdown."

Predictive maintenance already delivers results

Proof-of-concept projects and larger PdM implementations have shown that the benefits of predictive maintenance include: 

• Maximized revenue generation
• Reduced operational costs
• Increased ROI on machines
• Optimized maintenance team productivity

These and other benefits occur when applying predictive maintenance to:

• Critical process assets

• High-impact single points of failure

• Critical machinery

• Production line dependency assets

• Safety-critical equipment

Predictive maintenance success stories include: 

• An automotive manufacturing plant avoided $500,000 in prevented maintenance costs by preventing the catastrophic failure and rebuild of a stamping press.

• A global manufacturer saved over $230,000 annually in scrap costs per line by replacing damaged tools before they affected product quality.

• A retailer generated $250,000 in savings on replacement parts in each of their 10 fulfillment centers by preventing cross-belt sorter failures.

How predictive maintenance works

Predictive maintenance transforms raw operational data into actionable maintenance decisions. It uses a systematic process where:

• Sensors gather data from assets. 

• AI analyzes data in real time to predict failures.

• Real-time alerts trigger at the first signs of imminent failure.

Assets monitored by connected sensors continuously measure critical equipment parameters. Two common examples are: 

• Vibration analysis on high-speed rotating equipment.

• Oil analysis for lubricant condition assessment. 

Data collected from sensors flows seamlessly through a higher-level software system. AI and machine learning establish baselines and detect anomalies. Maintenance teams receive specific guidance rather than general alerts, transforming operational data into strategic maintenance advantages.

Implementing predictive maintenance succesfully

Successful initiatives systematically address infrastructure, organizational, and technical challenges through these strategic predictive maintenance implementation considerations: 

• Choosing easy-to-implement projects based on the current infrastructure

• Designing a proof-of-concept project around a machine’s impact on production.

• Determining KPIs to clearly show the results of an initial project. 

• Establishing success with one or two machines, then scaling. 

Choosing a predictive maintenance model

With many different predictive maintenance solutions and strategies available, it's essential to find the one that fits a facility's unique goals and requirements. The factors to consider when making this choice include:

• Current maintenance strategy
• Available IT resources
• Budget and vision 
• Capability to expand and scale

Generally, the three types of predictive maintenance models to compare are: 

• Route-based, manual monitoring or daily collection every 1-3 months
• Intermittent, or automated data collection, daily or hourly
• Real-time, or automated, continuous data collection

Overcoming common barriers to predictive maintenance success 

Hundreds of consultations and dozens of predictive maintenance projects have surfaced the most common challenges for a PdM program to be implemented or become successful:

• High initial cost
• Required significant infrastructure changes
• Knowledge or skills gaps
• Organizational resistance

Predictive maintenance best practices

Predictive maintenance best practices 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

Adopting predictive maintenance is not always a linear path

Developing a successful PdM program does not require a set of specific, incremental steps. Successful predictive maintenance relies on systematically enhancing existing processes rather than on complete operational transformation.

Organizations already using a condition-based monitoring system often have the easiest path to implementing PdM. However, successful use cases demonstrate that predictive maintenance can provide tangible results from various starting points.

Starting points vary by current maintenance maturity:

• Reactive maintenance facilities can implement sensors on equipment that cause plant-wide disruptions when failures occur.

• Time-based maintenance operations can integrate vibration analysis and other monitoring to optimize timing before replacing calendar-based approaches entirely.

• Condition monitoring programs can advance to predictive capabilities by incorporating machine learning algorithms that analyze existing sensor data patterns.

• Advanced facilities can scale pilot programs and use computerized maintenance management systems to automate work order generation and optimize resource allocation.

Predictive maintenance compared to other maintenance strategies

Maintenance strategies exist along a maturity spectrum, where each level builds capabilities while addressing cost and efficiency gaps:

1. Reactive maintenance operates on run-to-failure principles. Equipment repairs occur only after breakdowns. It’s initially cost-effective but generates unpredictable downtime and emergency repair costs.
2. Preventative maintenance, or scheduled maintenance, follows fixed time intervals regardless of equipment condition. This method transforms unplanned stops into planned stops, but results in unnecessary interventions or overlooked problems between scheduled intervals.
3. Condition monitoring uses equipment monitoring to assess real-time status and trigger maintenance when predetermined thresholds are exceeded. This approach typically responds to current problems rather than predicting future failures.
4. Predictive maintenance represents the most sophisticated approach, where advanced analyses of sensor data forecast equipment degradation patterns. Maintenance teams shut down equipment before failure, don't run parts to failure or replace them too soon, and keep spare parts inventory low. 

Maintenance evolution: What to expect in the next decade

The 2020 pandemic accelerated digital transformation efforts across manufacturing sectors. Since then, advances in IIoT sensors and big data have enhanced operational intelligence and promoted broader adoption of predictive maintenance across industries.

As the future of predictive maintenance continues toward widespread adoption, the technology and field-proven methodologies that drive it are on the brink of new developments:

Edge processing

Edge computing at the sensor level would alleviate the heavy burden of data transfer and storage. A sensor with edge processing capabilities could handle data analysis, establish baseline conditions, and only transmit detailed raw data when an anomaly occurs.

AI decision-making

Current technologies demonstrate promise that predictive maintenance programs can evolve toward fully autonomous decision-making where the AI model recommends specific maintenance actions and optimal scheduling without human intervention.

Digital Twins

Digital twins, or virtual replicas of physical processes that simulate real-world operations, promise to model production scenarios, predict equipment behavior, and more. Modeling without risking actual production could drive better operations planning and capital investment decisions. 

Enhanced sensors

As the cost of basic sensors drops, manufacturers can differentiate by producing models with greater sensing capabilities and built-in intelligence, or by harvesting ambient energy rather than requiring a power source. These advances would make PdM easier to afford and implement.

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 maintenance 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