Unexpected equipment failure can disrupt production, increase labor and repair costs, create safety concerns, and put customer commitments at risk. For large manufacturers operating complex facilities, even relatively short interruptions can have significant consequences.
That is why more organizations are investing in predictive maintenance, a data-driven approach that helps maintenance teams identify developing equipment problems before they result in failure. Unlike traditional maintenance strategies that rely primarily on fixed service intervals, predictive maintenance uses condition data to determine when an asset needs attention. Sensors, inspections, analytics and machine learning can reveal changes in vibration, temperature, pressure, electrical characteristics, and other indicators of equipment health.
Which predictive maintenance examples and strategies will work best for your organization depends on your facility’s equipment types, environment, workflows, and failure risk, as well as the availability of reliable condition data. Read on to see what predictive maintenance examples are recommended for your facility from Advanced Technology Services (ATS).
Overview of predictive maintenance programs
A predictive maintenance program creates a structured process for collecting equipment-condition data, identifying abnormalities and turning those insights into maintenance actions.
Benefits of predictive maintenance
- Less unplanned downtime
- More efficient maintenance labor
- Earlier identification of developing failures
- Longer equipment life
- Better maintenance planning
- Improved spare-parts planning
- Reduced unnecessary maintenance
- Greater operational reliability
- Improved worker safety
For manufacturers considering predictive technologies, the goal is a repeatable reliability process that turns condition information into useful maintenance decisions. The goal is to build a repeatable reliability process that turns condition information into useful maintenance decisions. Start by evaluating your most production-critical assets, their common failure modes and the consequences of unexpected downtime.
Preventive and predictive maintenance: Choosing the right mix
Predictive and preventive maintenance should not necessarily be treated as competing strategies. Preventive maintenance schedules work well when maintenance requirements are predictable and closely related to time, operating hours or usage. Examples include routine lubrication, filter changes, inspections, and manufacturer-required service.
Predictive maintenance is generally more valuable when an asset has measurable failure indicators and when unexpected failure would have substantial operational or financial consequences. For example, routinely replacing an inexpensive filter may make more sense than installing sensors to predict when it will fail. A critical production motor, gearbox, compressor, or spindle may justify continuous condition monitoring because an unexpected breakdown could stop an entire process.
How to choose between them? Consider asset criticality, failure patterns, monitoring costs, repair costs, downtime consequences, and the availability of meaningful condition indicators. In many facilities, what works best is a hybrid strategy: preventive maintenance for predictable routine requirements and predictive monitoring for critical assets where condition data can improve decision-making.
Predictive maintenance process
A successful predictive maintenance process connects technology with existing maintenance workflows.
Basic cycle
1. Select critical equipment.
2. Identify likely failure modes.
3. Determine which conditions can indicate those failures.
4. Install appropriate sensors or establish inspection routes.
5. Collect and transmit condition data.
6. Analyze trends and anomalies.
7. Generate actionable alerts.
8. Plan and execute maintenance.
9. Document findings and outcomes.
10. Feed those results back into the program.
The last step is especially important. A predictive maintenance program becomes more useful as teams learn which signals accurately predict problems and which generate unnecessary alerts.
Equipment selection and sensor installation
Start with equipment whose failure creates the greatest operational risk. Critical motors, pumps, compressors, gearboxes, bearings, conveyors, production machinery, and utilities are common candidates. Sensor selection should correspond to the failure mode. Vibration sensors can identify bearing or alignment problems. Temperature sensors can reveal overheating. Pressure sensors can monitor compressors and fluid systems. Current monitoring can identify electrical or motor abnormalities.
Placement and calibration matter as much as sensor selection. Poorly positioned sensors can produce misleading data, so installation should account for equipment configuration, operating conditions and environmental factors.
Talk to an ATS expert to see which assets and monitoring points should be prioritized. A reliability assessment can help avoid spending money collecting data that does not lead to useful maintenance decisions.
Data collection and transmission
Once sensors are installed, data must move securely and reliably from the equipment to the systems where it will be analyzed. Some applications benefit from edge processing, where information is evaluated close to the machine. This can reduce latency and data-transfer requirements. Cloud-based systems can provide centralized storage, analytics, and visibility across machines or facilities. Integration matters as predictive insights become more valuable when they can connect directly to maintenance scheduling, reporting and work-order processes.
Data processing and analysis
Raw sensor readings are not maintenance recommendations. Data should first be cleaned, normalized and placed in operating context. Analysts and software can then establish baselines, track trends and identify anomalies. The priority should be an actionable output. A technician usually needs to know which machine is affected, what abnormal condition was detected, how serious it appears, and what action should be considered. Not simply that a sensor produced an unusual number.
Machine learning models and deployment
Machine learning can improve anomaly detection when facilities have sufficient high-quality data. Common approaches include classification models that identify known failure conditions, anomaly-detection models that flag deviations from normal behavior, regression models that forecast degradation, and time-series models that evaluate changes over time.
Models should be validated using metrics appropriate to the application, including precision, recall, false-alert rates. and the amount of warning provided before failure. What is recommended after deployment? Continue validating models against actual technician findings. Equipment, processes and operating environments change, so models may require periodic retraining and governance.
Predictive modeling and remaining useful life
Some advanced systems estimate Remaining Useful Life (RUL), or the approximate period an asset can continue operating before maintenance or replacement is required. RUL models can combine historical failure information, current sensor readings, operating loads, and degradation patterns. Rather than automatically triggering maintenance whenever a value changes, an RUL estimate can help planners determine whether work should occur immediately, during the next planned outage or at a later service interval. Even an approximate RUL estimate helps planners schedule the work
Alert generation and maintenance planning
Predictive alerts should use defined thresholds, confidence levels and escalation rules. A minor deviation may justify continued monitoring. A rapidly worsening vibration trend on a production-critical bearing could justify inspection during the next available maintenance window. A high-confidence indication of imminent failure may require immediate intervention. Alerts should ideally connect with CMMS workflows, so teams can generate or prioritize work orders without creating a separate disconnected process.
Maintenance execution and the feedback loop
Technicians remain essential to predictive maintenance. When an alert is generated, technicians should receive enough information to inspect the suspected problem efficiently. Their findings should then be documented. That information closes the feedback loop and can improve future analytics.
Predictive maintenance examples by industry
There is no universal predictive strategy. The following predictive maintenance examples illustrate how organizations can match monitoring technologies to specific assets and failure modes.
Oil and gas industry
Pipeline pumps can be monitored using vibration, temperature, pressure, and flow information. Changes may indicate bearing deterioration, cavitation, imbalance, or other developing mechanical problems. Transmission pipelines can also use ultrasonic inspections to evaluate wall thickness and identify potential corrosion or material loss. For remote or offshore assets, local sensors combined with edge processing and centralized remote monitoring can reduce the need for unnecessary inspection trips while giving reliability teams visibility into equipment condition.
Gas industry: Compressors and pipelines
Compressors are strong candidates for condition monitoring because pressure, temperature and vibration trends can reveal deteriorating operating conditions. Pipeline monitoring can also incorporate leak detection, pressure analysis, ultrasonic methods, and corrosion inspections. Alerts can then trigger verification, inspection or planned repair based on severity.
Manufacturing
Computer Numerical Control (CNC) spindle monitoring is one of the most common predictive maintenance examples in discrete manufacturing. Vibration trends can indicate bearing wear, imbalance or other mechanical changes before machining quality or uptime is affected. Acoustic monitoring can help identify deteriorating conveyor rollers, while robotic welders may be monitored using current, temperature, vibration, and process-performance information.
Power generation and utilities
Gas turbines can use vibration and exhaust-condition monitoring to identify rotor imbalance, misalignment and abnormal combustion conditions. Transformers can use dissolved gas analysis to detect gases associated with insulation degradation, overheating and electrical faults. Thermal imaging can provide another layer of protection by identifying abnormal hot spots in electrical equipment.
Fleet and transportation
Vehicle telematics can monitor engine temperature, diagnostic codes, operating hours, and other health indicators to identify vehicles that need attention before roadside failure occurs. Brake temperature monitoring can similarly identify abnormal heat associated with dragging brakes, excessive friction or other developing issues.
Food, beverage and pharmaceutical
Refrigeration compressors can be monitored using pressure and temperature information to identify abnormal operation and potential leaks. In pharmaceutical environments, autoclave cycle data can be trended to identify changes in temperature, pressure or cycle stability that may indicate equipment issues requiring investigation.
Facilities and building management
Predictive methods also apply beyond production machinery. HVAC systems can use delta-T measurements, vibration and motor-current monitoring to evaluate performance. Refrigeration systems can continuously track temperatures and compressor conditions. These applications can help facilities teams detect degrading performance before it affects production environments, energy consumption or temperature-sensitive materials.
Condition monitoring techniques
Vibration analysis and motor circuit analysis
Vibration analysis is particularly useful for rotating machinery. Changes in vibration amplitude or frequency can indicate imbalance, misalignment, looseness, bearing deterioration, and gear defects. Motor Circuit Analysis (MCA) evaluates the electrical characteristics of motors and associated circuits.
Depending on the testing approach, measurements can include resistance, impedance, inductance, phase balance, and insulation-related indicators. Facilities should establish equipment-specific baselines rather than relying solely on universal limits. Testing frequency should then reflect asset criticality, operating conditions and historical failure patterns.
Oil analysis and particle counting
Oil analysis provides insight into both lubricant condition and machine condition. Samples can be evaluated for contamination, viscosity changes, wear metals, and particles. Increasing particle concentrations or changes in particle size can indicate developing component wear. Rather than using one particle-count threshold for every machine, limits should be based on equipment type, lubricant requirements, manufacturer guidance, operating conditions, and established cleanliness targets.
Thermography, ultrasonic and acoustic methods
Thermography uses infrared imaging to identify abnormal heat patterns. It is particularly useful for electrical systems, bearings, mechanical connections, and equipment where excessive friction or resistance produces heat. Ultrasonic testing can identify compressed-air and vacuum leaks, electrical abnormalities and certain mechanical problems that produce high-frequency sound.
If your organization is interested in machine health and condition monitoring, start by talking with an ATS expert. We can match monitoring technology to known equipment failure modes rather than deploying the same sensor strategy everywhere.
Maintenance management, costs and equipment lifespan
The business case for predictive maintenance should account for more than sensor costs. Start by establishing current annual costs for reactive repairs, emergency labor, lost production, expedited parts, scrap, and preventive maintenance. Then compare those costs with the expected program investment and the value of avoided failures.
A simple Return On Investment (ROI) calculation can be expressed as:
ROI = (avoided downtime + maintenance savings + avoided secondary damage – program cost) ÷ program cost
The benefits of predictive maintenance may also extend beyond first-year savings. Earlier intervention can reduce secondary damage and help equipment remain serviceable longer.
Building a predictive maintenance strategy with ATS
Technology alone does not create reliability. Manufacturers also need processes, skilled technicians, maintenance planning, and the expertise to translate condition information into action. For organizations beginning their predictive journey, a focused pilot can establish value before expanding monitoring coverage.
A reliability engineering assessment can also help identify critical assets, failure modes and appropriate technologies. Once results are validated, manufacturers can develop standardized approaches for additional equipment, production lines or sites. This scalability can be particularly important for multi-site manufacturers seeking consistent reliability practices.
The strongest predictive maintenance program gives maintenance teams useful information early enough to take the right action. Talk to an ATS expert to evaluate your current maintenance strategy, identify equipment suited for predictive monitoring and determine what is recommended for your facility.
