Maintenance teams today face a difficult equation. They're responsible for hundreds of assets, often with fewer technicians than they had five years ago, and the cost of unplanned downtime keeps climbing. According to a 2024 Siemens report, the world's 500 largest companies lose approximately $1.4 trillion annually to unplanned downtime, equivalent to 11% of their total revenues. The pressure to maximize uptime has never been higher, and the margin for error has never been thinner.
This reality is pushing more organizations away from two traditional approaches that no longer fit the operating environment. Time-based preventive maintenance services equipment on fixed schedules regardless of actual condition, which means teams often perform unnecessary work on healthy machines while missing problems that develop between intervals.
Reactive maintenance waits for failure before acting, keeping teams in a perpetual firefighting mode and guaranteeing the most expensive possible repairs.
Condition monitoring offers a third path. By tracking real-time data from equipment and identifying early signs of wear or malfunction, it allows maintenance decisions to be based on the actual machine state rather than arbitrary schedules or emergency alarms. The practice is both a set of techniques and a capability that scales based on how well those techniques integrate with broader maintenance systems.
This article defines condition monitoring, explains how it works, surveys the primary techniques, and examines what distinguishes programs that deliver ROI from those that simply generate data.
Key Points
- Condition monitoring is the practice of tracking real-time equipment data to detect early signs of wear or failure, enabling maintenance decisions based on actual machine state rather than fixed schedules or reactive response.
- Effective programs combine multiple techniques based on asset type and failure modes, with vibration analysis serving as the foundational method for rotating equipment, and complementary techniques such as oil analysis, thermography, and ultrasonics addressing specific gaps.
- The value of condition monitoring depends on integration. Programs that connect detection to diagnosis to work order generation, deliver measurable ROI, while those that stop at alerting generate data without driving action.
- Tractian's condition monitoring solution unifies wireless sensors, AI-powered diagnostics, and an integrated CMMS to create a closed-loop system where equipment insights convert directly into maintenance execution.
What Is Condition Monitoring?
Condition monitoring is the practice of continuously or periodically measuring equipment parameters to detect early signs of degradation or failure while assets are still operating.
Condition monitoring is a pretty straightforward concept. Instead of waiting for a machine to break or servicing it on a fixed calendar, teams track real-time indicators of equipment health. Condition-monitoring sensors capture variables such as vibration, temperature, pressure, oil condition, and sound. Changes in these signals often reveal developing problems long before they become visible or audible to human inspectors. A bearing generating excess heat, a motor vibrating outside its normal range, or a lubricant contaminated with metal particles all indicate something is wrong inside the machine. Condition monitoring catches these signals and translates them into maintenance decisions.
The practice is also called condition-based monitoring or condition-based maintenance (CbM), and these terms are often used interchangeably. What distinguishes condition monitoring from other maintenance strategies is its reliance on the actual state of the equipment.
Time-based preventive maintenance schedules interventions at fixed intervals regardless of whether the machine needs attention. Reactive maintenance waits until equipment fails. Condition monitoring sits between these approaches, using evidence from the machine itself to determine when action is required.
This distinction matters because condition monitoring provides the data foundation for predictive maintenance. Predictive maintenance takes condition data and applies analytics, machine learning, or failure models to forecast when equipment will fail and recommend optimal intervention timing. Condition monitoring supplies the raw insight. Predictive maintenance turns that insight into a forward-looking strategy. Without reliable condition data, predictive maintenance has nothing to work with.
The approach applies most directly to rotating equipment with moving parts, such as pumps, motors, gearboxes, compressors, fans, and turbines. Any asset with bearings, seals, or lubrication systems is a strong candidate. But the principle extends broadly. If a machine generates measurable signals during operation, those signals can reveal its health.
How Condition Monitoring Works
Condition monitoring captures data from sensors installed on equipment, analyzes it against established baselines, and triggers alerts when deviations indicate developing problems.
The process begins at the asset level. Sensors track variables tied to mechanical health: vibration patterns, surface temperature, electrical current draw, pressure levels, and rotation speed. These measurements are recorded and transmitted to a central system, often cloud-based, where they are analyzed.
The condition-monitoring software compares incoming values against historical baselines that represent normal operating behavior. When a pattern shifts beyond expected thresholds, the system flags the deviation. A motor vibrating beyond its usual range, a pump running hotter than normal, or a gearbox drawing more current than it should will all trigger attention.
What happens next determines whether the program delivers value or just generates noise. Effective condition monitoring moves through a progression that transforms raw signals into coordinated action.
Detection identifies that something changed. To understand the change, we need to know when the anomaly appeared, how early it was caught, and how much time exists before the issue becomes critical. Diagnosis determines what the fault actually is, where it originated, what stage it has reached, and which components are affected. A vibration spike might indicate bearing wear, misalignment, imbalance, or looseness.
Diagnosis narrows the possibilities to a specific cause, but prognosis forecasts consequences, such as the severity of the issue, how quickly it's progressing, and what will happen if left unaddressed.
This stage estimates the timeline to failure and the risks of continued operation. The predictive maintenance software translates all of this into maintenance decisions, such as when to intervene, how to schedule repairs around production, and what resources are required to complete them.
Systems that handle only detection leave teams with alerts but no direction. Knowing that vibration increased is useful, but knowing that the increase indicates inner race bearing wear with an estimated three weeks to failure is actionable. The distinction separates monitoring that informs decisions from monitoring that simply creates more data to interpret.
In practice, condition monitoring operates in two primary modes. Trend monitoring tracks continuous measurements over time to identify gradual changes, making it particularly effective for slow-developing issues like bearing degradation or progressive misalignment. Asset health monitoring involves periodic assessments of equipment health while it runs, such as thermal imaging scans or oil sample analysis, providing snapshots that complement continuous data streams.
Both modes contribute to a fuller picture of asset health. The value lies not in any single reading but in the visibility that accumulates over time, revealing patterns that manual inspection would miss and enabling intervention before minor issues become major failures.
Types of Condition Monitoring Techniques
Different assets and failure modes require different condition monitoring techniques, with vibration analysis being the most widely applicable for rotating equipment.
Condition monitoring is not a single method.
The right technique depends on the asset type, the failure modes being tracked, and the operating environment. A motor benefits from vibration analysis. A hydraulic system needs to track the oil condition. An electrical panel calls for thermal imaging. Most industrial programs combine multiple methods to cover the range of equipment and failure signatures present in their facilities.
Vibration analysis
Vibration analysis is the most widely used and most accurate condition-monitoring technique for rotating machinery. Every machine with moving parts generates a vibration signature during operation, a pattern of frequencies and amplitudes that reflects its mechanical state.
When components begin to wear, loosen, or fall out of alignment, that signature changes. High-frequency sensors detect these shifts and translate them into data that reveals what's happening inside the equipment.
The technique excels at identifying imbalance, misalignment, bearing defects, mechanical looseness, and gear wear. Studies indicate that more than 40% of rotating equipment breakdowns relate to bearing issues, making vibration monitoring particularly valuable for early detection. In many cases, vibration analysis can identify bearing wear 12 to 18 months before replacement becomes necessary, giving maintenance teams substantial lead time to plan interventions.
For motors, pumps, fans, gearboxes, and compressors, vibration analysis is typically the foundational monitoring technique.
Infrared thermography
Infrared thermography uses thermal imaging to detect abnormal temperature patterns in equipment. Every machine emits heat during operation, and when components fail, that thermal profile changes. Friction from worn bearings, resistance from loose electrical connections, and degraded insulation all generate heat signatures that thermographic cameras capture and display as visual heat maps.
The technique is non-invasive and can be performed while the equipment runs, making it ideal for inspecting critical systems without interrupting operations. It detects loose or overloaded electrical connections, misaligned bearings, insulation failures in electrical panels, and fluid or gas leaks in pressurized systems.
Thermography is particularly valuable for electrical infrastructure, where hot spots often indicate problems that other monitoring methods cannot detect.
Oil analysis
Oil analysis evaluates the condition of lubricant to reveal internal wear and contamination that would otherwise remain hidden. As machines operate, their lubricants collect trace elements: metal particles from wear, combustion byproducts, and external contaminants like water or dust. Analyzing these lubricants provides insight into the condition of internal components without disassembling anything.
Typical oil analysis examines wear metals (iron from gears, copper from bearings), contaminant levels, additive depletion, viscosity, and acidity. Elevated silicon might indicate external dirt ingress. High iron levels paired with copper could indicate simultaneous gear and bearing degradation.
The technique is critical for gearboxes, compressors, engines, and hydraulic systems where lubricant health directly affects performance and where internal wear is otherwise difficult to detect.
Ultrasonic/Ultrasound analysis
Ultrasonic analysis detects high-frequency sound waves beyond the range of human hearing. When friction increases, pressure leaks occur, or electrical discharges happen, equipment produces acoustic signals that ultrasonic sensors can capture and interpret.
The technique is particularly sensitive to the earliest signs of failure. A bearing in the first phase of wear may appear normal under vibration analysis, but will emit ultrasonic noise due to increased microscopic friction.
Ultrasonic monitoring identifies pressure and vacuum leaks in compressed air systems, early-stage bearing failure, poor lubrication conditions, steam trap malfunctions, and electrical arcing or corona discharge. Because ultrasonic tools can isolate specific sound signatures even in noisy industrial environments, they work well for targeted inspections and complement vibration data by catching issues at earlier stages.
Motor circuit analysis
Motor circuit analysis assesses electric motor health by evaluating the integrity of electrical circuits without requiring the motor to run during testing. By applying a low-voltage signal to motor windings and analyzing the response, maintenance teams detect problems in the stator, rotor, insulation, and connections before those issues cause failure during operation.
The technique identifies insulation degradation, open or short circuits in windings, rotor bar defects, loose or corroded connections, and phase imbalances. Because it can be performed offline, motor circuit analysis is flexible for use during planned downtime, before startup after storage, or as part of incoming equipment inspection.
For facilities where electric motors drive critical assets, this technique provides visibility into electrical health that mechanical monitoring methods cannot offer.
Combining techniques for coverage
The most effective condition monitoring programs layer techniques based on asset criticality analysis and failure modes. Vibration analysis covers rotating equipment broadly. Oil analysis addresses internal wear in lubricated systems. Thermography monitors electrical infrastructure and heat-generating components. Ultrasound catches leaks and early-stage mechanical issues. Motor circuit analysis ensures electrical integrity.
No single technique covers everything. The question is which combination provides adequate visibility into the failure modes that matter most for a given facility's assets and operating context.
Connectivity and Infrastructure
The choice between wired and wireless condition monitoring systems depends on asset criticality, environmental conditions, data requirements, and scalability goals.
Deploying sensors is only part of the infrastructure decision. How those sensors transmit data, how reliably they operate in industrial environments, and how easily the system scales to additional assets all affect whether a monitoring program succeeds or stalls.
Wired systems offer stability and reliability. They provide continuous power and high data transmission rates, making them well-suited for high-speed machinery or environments with heavy electromagnetic interference near variable-frequency drives or high-voltage equipment.
The limitation of wired infrastructure is complexity. Running cable across a large facility takes significant time and planning, especially in hard-to-reach or hazardous areas. Once installed, wired systems are difficult to modify or scale without additional infrastructure investment.
Wireless systems have closed the reliability gap that made earlier generations unsuitable for serious monitoring programs.
Advanced wireless sensors are flexible, cost-effective, and fast to deploy. They operate on battery power, have multi-year lifespans, and transmit data via cellular networks, proprietary radio frequencies, or mesh protocols that don't rely on plant Wi-Fi. This independence from existing network infrastructure simplifies deployment and avoids security concerns about connecting sensors to corporate systems.
For facilities looking to scale condition monitoring across many assets, especially those spread across multiple buildings or floors, wireless systems offer a practical path that wired approaches struggle to match.
Tractian's Smart Trac Ultra exemplifies this approach, using 4G/LTE cellular connectivity to transmit data without relying on plant networking infrastructure, with a communication range of up to 330 feet indoors and a battery life of three to five years.
The shift toward IoT-connected monitoring has also changed what's possible with condition data. Cloud platforms aggregate information from multiple assets across locations, enabling centralized visibility that was previously impractical.
AI and machine learning processes large volumes of data to detect patterns that would take human analysts hours or days to identify. Remote access allows fewer technicians to monitor more equipment without being physically present, a critical capability for facilities facing labor constraints or managing distributed operations.
The choice of infrastructure isn't just about sensors. Rather, it's a consideration of whether the system architecture supports the kind of analysis and accessibility that makes condition monitoring actionable.
Benefits of Condition Monitoring
Condition monitoring delivers measurable operational and financial benefits by catching problems early, eliminating unnecessary work, and enabling maintenance teams to act with precision rather than react to emergencies.
The case for condition monitoring rests on outcomes, not features. What changes when teams have continuous visibility into equipment health?
Avoiding unplanned downtime is typically the largest financial benefit. Early detection allows repairs to be scheduled before breakdowns occur. Teams plan interventions around production schedules rather than scrambling to respond when a critical asset fails unexpectedly. The financial impact is substantial.
According to MaintainX's 2024 State of Industrial Maintenance report, the average cost of one hour of unplanned downtime reaches approximately $25,000 and can exceed $500,000 for larger organizations. Condition monitoring doesn't eliminate all downtime, but it shifts the balance from unplanned stops to planned interventions where costs are controlled, and production impact is minimized.
Protecting related assets addresses a risk that reactive maintenance ignores. Failures rarely stay isolated. A seized motor strains the gearbox it drives. A worn bearing causes shaft misalignment that damages seals and couplings downstream.
By catching the initial problem early, condition monitoring limits collateral damage and prevents single-point failures from cascading into multi-asset breakdowns that multiply repair costs and extend downtime.
Eliminating unnecessary maintenance challenges the assumption that more preventive work means better reliability. Fixed-interval schedules often service equipment that doesn't need attention while missing problems that develop between intervals. Condition monitoring reveals which machines are running fine and which actually require intervention.
When maintenance happens only when evidence supports it, teams stop wasting labor, parts, and production time on work that adds cost without adding value.
Improving maintenance efficiency changes how technicians spend their time. Without condition data, troubleshooting starts from scratch. Technicians arrive at a problem with no advance information and work through diagnostic steps to identify the fault. With condition monitoring, they arrive knowing what's wrong. The diagnosis is already complete.
This clarity accelerates work orders, improves first-time fix rates, and allows teams to focus their efforts where they matter rather than spreading attention across assets that don't need it.
Enhancing safety reduces exposure to the hazards that accompany reactive maintenance. Emergency repairs often happen under pressure, in unfavorable conditions, with equipment in unpredictable states.
Condition monitoring minimizes these situations by enabling planned interventions. It also reduces the need for manual inspection routines that put technicians in close proximity to running equipment or require access to difficult locations. Remote visibility into asset health means fewer risky physical checks.
Extending asset life and improving OEE accumulates over time. Equipment running within optimal parameters, with issues addressed before they cause damage, lasts longer and performs more consistently. Detected inefficiencies lead to corrections that reduce energy waste.
Higher availability and better throughput result from assets that spend more time producing and less time being broken or repaired. These gains compound as condition-monitoring programs mature and baseline data improve.
5 Steps to Building a Condition Monitoring Program
Effective condition monitoring programs follow a structured sequence:
- Establish context from historical data
- Prioritize assets
- Deploy sensors matched to failure modes
- Define baselines
- Integrate monitoring data with maintenance workflows.
The difference between programs that deliver ROI and those that generate unused data often comes down to how they're built. Jumping straight to sensor installation without groundwork leads to gaps in coverage, unclear ownership, and alerts that no one knows how to interpret. A deliberate sequence sets the foundation for sustainable results.
Step 1: Collect historical data.
Before adding sensors, understand the past behavior of the assets you plan to monitor. Gather maintenance logs, failure reports, runtime hours, and repair history. This data helps prioritize which equipment needs monitoring first and provides context for interpreting future anomalies.
If vibration levels spiked before a bearing failure last year, that pattern becomes valuable when setting alert thresholds. Historical context also reveals which assets have been problematic and which failure modes have caused the most disruption, guiding where to focus initial efforts.
Step 2: Prioritize assets by criticality.
Not every asset needs to be monitored immediately, and trying to cover everything at once delays results. Focus on equipment where failure causes the greatest impact: production loss, safety risk, quality problems, or cascading damage to connected systems.
Criticality rankings guide deployment sequence and ensure early wins that justify continued investment. A facility might have hundreds of motors, but the five that drive critical production lines matter more than the dozens serving auxiliary functions. Start where the stakes are highest.
Step 3: Install sensors matched to failure modes.
Sensor selection depends on what you're trying to detect. Vibration sensors are suitable for rotating equipment where imbalance, misalignment, and bearing wear are primary concerns. Ultrasonic sensors catch leaks and early-stage friction issues. Thermography addresses electrical systems and heat-generating components. Oil analysis monitors internal wear in lubricated systems.
Match the technique to the asset type and the failure modes that matter most. Plan for practical considerations as well: mounting locations, signal range, maintenance accessibility, and environmental conditions that could affect sensor performance.
Step 4: Establish baselines.
Data collection begins as soon as sensors come online, but meaningful analysis requires understanding what normal looks like. During the baseline period, assets are monitored under regular operating conditions to define healthy behavior. These baselines become reference points for future anomaly detection.
Any significant deviation from established patterns is what triggers alerts. Baseline establishment typically takes two to four weeks of normal operation, though complex or variable-load equipment may require longer observation to capture the full range of expected behavior.
Step 5: Integrate with maintenance workflows.
This step separates monitoring programs that drive action from those that produce dashboards no one checks. Condition data becomes valuable when it flows into maintenance systems and triggers a response. Connect alerts to work order generation, inspection scheduling, and parts procurement. Define what happens when an anomaly appears: who receives the notification, what actions they take, and how resolution is tracked.
The goal is a closed loop where detection leads to diagnosis, diagnosis leads to a work order, and the work order leads to intervention before failure occurs.
The value of condition monitoring increases when insights translate directly into maintenance actions rather than sitting in a separate system waiting for someone to notice. Integration is what makes the difference between data and decisions.
Condition Monitoring with Tractian
Tractian's condition monitoring system combines Smart Trac Ultra wireless vibration sensors with automatic failure-detection diagnostics and a natively integrated AI-powered CMMS to create a closed-loop system that converts equipment insights directly into maintenance actions.
Tractian solves the challenge that many condition-monitoring implementations don’t account for: the gap between detection and response. Tractian's approach eliminates that gap by unifying sensing, diagnosis, and execution in a single platform.
Smart Trac Ultra provides the sensing layer. The wireless vibration sensor monitors vibration, temperature, runtime, and RPM in real time, capturing the data streams that reveal equipment health. What distinguishes it from basic vibration sensors is the built-in intelligence. Auto Diagnosis™ analyzes incoming data and automatically identifies all major failure modes, from bearing wear and misalignment to cavitation and lubrication problems.
Tractian’s industrial-grade sensor hardware is built for industrial reality. Battery life spans three to five years under default settings. Cellular connectivity (4G/LTE) transmits data without relying on Wi-Fi infrastructure, reducing network security risks and coverage gaps. The sensor carries ATEX, IECEx, and NFPA certifications for hazardous locations. Features like Always Listening™ handle intermittent machines that cycle on and off, while RPM Encoder™ provides accurate analysis for variable-speed equipment that confuses simpler monitoring approaches.
The condition-monitoring software serves as the intelligence layer, converting raw sensor data into actionable insights. When the system detects an anomaly, it doesn't issue vague alerts. It provides a specific diagnosis, an assessment of severity, and a recommended course of action.
What completes the system is the integration with Tractian's AI-powered CMMS. This connection transforms condition monitoring from an observation tool into an execution platform. When Smart Trac Ultra detects a developing fault and the AI diagnoses the problem, that insight flows directly into its work order management software. Technicians receive assignments with the diagnosis already attached. Parts can be identified and staged. The repair gets scheduled around production requirements rather than interrupting them. Once work is complete, the resolution feeds back into the system, closing the loop and refining future detection.
This integration is where Tractian's approach delivers compounding value. It exemplifies what condition monitoring looks like when fully integrated. The sensor detects the problem, the AI diagnoses it, and the CMMS ensures maintenance acts on it. That closed loop is what separates programs that deliver ROI from those that generate data no one uses.
For teams evaluating condition monitoring systems, the ultimate question is whether it will produce confident, prioritized, actionable decisions at scale. This is the standard that Tractian was built to meet.
Explore Tractian condition monitoring solutions to see how decision-grade condition monitoring can transform your maintenance team’s impact.
What Industries Benefit Most from Decision-Grade Condition Monitoring?
The gap between detection and decision affects any facility running critical equipment, but some operating contexts amplify its consequences. Lean teams, compressed production schedules, remote or hazardous assets, and seasonal constraints all reduce the margin for interpretation delays and manual verification.
For these industries, monitoring that prioritizes, diagnoses, and closes the loop between signal and action repositions the maintenance team to deliver bottom-line efficiencies.
- Automotive & Parts: High-speed production lines leave no room for interpretation delays, making diagnostic specificity and prioritized alerts essential for protecting throughput without overburdening lean maintenance teams.
- Fleet: Shop equipment failures directly affect vehicle turnaround, and decision-grade monitoring ensures technicians act on confirmed issues rather than chasing ambiguous alerts across multiple service bays.
- Manufacturing: Continuous operation of motors, pumps, and conveyors generates high alert volumes, and built-in prioritization determines whether teams focus on what matters or drown in undifferentiated notifications.
- Oil & Gas: Remote assets and hazardous environments make manual verification impractical, elevating the need for monitoring systems that deliver confident, actionable diagnoses without requiring on-site confirmation.
- Chemicals: Process stability depends on catching issues early, and diagnostic clarity ensures teams understand failure modes precisely enough to intervene before minor anomalies escalate into process disruptions.
- Food & Beverage: Tight production schedules and sanitation requirements limit maintenance windows, making it critical that condition insights translate directly into scheduled tasks without delays in interpretation.
- Mills & Agriculture: Seasonal processing creates high-stakes periods where every alert demands immediate triage, and criticality-based prioritization ensures limited maintenance resources focus on harvest-critical equipment first.
- Mining & Metals: Harsh operating conditions and heavy equipment generate complex vibration signatures, requiring AI-driven diagnostics that distinguish genuine faults from environmental noise without specialist interpretation.
- Heavy Equipment: Variable loads and demanding duty cycles produce inconsistent baselines, making contextual diagnostics essential for identifying true anomalies and avoiding false positives that erode operator trust.
- Facilities: Distributed assets across multiple sites require centralized visibility with local relevance, and decision-grade monitoring ensures building engineers receive prioritized, actionable alerts rather than raw data streams.
FAQs: Frequently Asked Condition Monitoring Questions
What is the difference between condition monitoring and predictive maintenance?
Condition monitoring is the practice of collecting equipment health data through sensors and analysis techniques. Predictive maintenance is a strategy that uses condition-monitoring data to forecast failures and schedule interventions at optimal times. Condition monitoring provides the data foundation. Predictive maintenance applies that data to anticipate problems and plan responses before failures occur.
What does condition monitoring analysis involve?
Condition monitoring analysis is the process of interpreting sensor data to identify anomalies, diagnose faults, and determine appropriate maintenance responses. It includes comparing current measurements against established baselines, recognizing patterns associated with specific failure modes, and assessing severity and progression.
What factors matter when comparing condition monitoring systems?
Key factors include sensor capabilities (frequency range, tracked parameters, environmental ratings), data transmission methods (wired, wireless, cellular), diagnostic intelligence (basic alerts versus AI-powered diagnosis), and integration with maintenance workflows. When comparing condition monitoring systems, prioritize platforms that connect detection to action rather than stopping at dashboards.
How does condition monitoring support asset health management?
Condition monitoring provides real-time equipment data on which asset health management strategies depend. By continuously tracking vibration, temperature, and other indicators, it reveals the current machine state and degradation trends. This visibility allows maintenance and reliability teams to prioritize interventions, forecast remaining useful life, and align maintenance decisions with broader asset health management objectives.
How long does it take to see results from a condition monitoring program?
Baseline establishment typically takes two to four weeks of normal equipment operation. Early fault detection can begin as soon as baselines are set and the system understands normal behavior. Measurable ROI through reduced downtime, avoided failures, and optimized maintenance typically appears within three to six months of deployment, with results compounding as historical data accumulates.
What failure modes can Tractian's condition monitoring detect?
Smart Trac Ultra's Auto Diagnosis™ identifies all major failure modes, including bearing wear, misalignment, imbalance, mechanical looseness, lubrication issues, cavitation, gear defects, and temperature anomalies. The AI platform provides specific diagnoses and prescriptive recommendations for each detected fault. Detection capabilities span most types of rotating equipment found in industrial manufacturing environments.
