Condition Monitoring: What It Is, Benefits, and How to Apply

Maintenance teams today are under a lot of pressure with hundreds of assets to keep running and fewer hands on deck. It’s simply not possible to inspect every machine as often as needed. 

That’s why more teams are shifting toward a smarter, more strategic approach.

Condition Monitoring enables teams to detect signs of wear, misalignment, or overload by monitoring asset health in real time. Maintenance efforts can identify and resolve these issues long before those turn into costly downtime.

Condition monitoring is the lever companies use to elevate their maintenance programs from reactive ‘firefighting’ mode to proactive, cost-saving operations. Businesses that make this shift experience fewer breakdowns, longer asset life, and increased productivity and efficiency.

In this article, we’ll break down the actionable steps required to bring the value it can deliver to your operation.

What Is Condition Monitoring?

Condition monitoring is the practice of tracking real-time data from machines to detect early signs of wear, malfunction, or failure before breakdowns happen.

This strategy replaces guesswork with hard data. Instead of reacting to issues or sticking to rigid maintenance intervals, it tracks real-time data from equipment to detect signs of wear or malfunction while the asset is still operating. 

Changes in vibration, heat, pressure, or even sound can signal that something’s off, often before it’s visible to the naked eye.

Because of this, it’s a key part of any predictive maintenance program. By identifying problems early, teams can avoid unplanned downtime, extend asset life, and confidently plan maintenance.

Condition monitoring benefits any rotating equipment with moving parts, not just large systems or high-end plants. This includes pumps, motors, gearboxes, and compressors.

How Does Condition Monitoring Work

Condition monitoring works by continuously collecting data from sensors installed on equipment, analyzing that data in real time, and triggering alerts when a deviation from normal behavior is detected.

The monitoring process begins at the asset level. Sensors track variables like vibration, temperature, electrical current, pressure, and speed, which are key indicators of mechanical health. 

These signals are recorded and transmitted to a central system (often cloud-based), where the real analysis begins.

Once the data arrives, the monitoring software compares the incoming values to historical baselines. 

If a pattern shifts (e.g., a motor starts vibrating beyond its usual range), that deviation gets flagged. The system can then alert the maintenance team. These alerts typically include context around what the anomaly might mean and what kind of failure it could indicate.

In practice, condition monitoring can operate in two main modes:

• Trend Monitoring: Tracks continuous measurements to identify gradual changes over time. It's especially useful for detecting progressive issues like bearing wear or misalignment.
• Condition Checking: Involves periodic assessments of an asset’s condition while it’s running. For example, inspecting oil quality or taking thermal images to check for hotspots.

Whether the system is always on or checked at intervals, the value lies in visibility backed by real-time data. Instead of waiting for noise, heat, or failure to force action, condition monitoring gives maintenance teams foresight.

Condition Monitoring and The Internet of Things

The Internet of Things (IoT) is turning what used to be a specialized, local process into a fully connected, real-time system that scales across entire operations.

IoT in condition monitoring means that machines, sensors, and software platforms are all linked and synced through the internet. Data no longer sits on a local device or needs to be collected manually. 

Instead, sensors send data continuously to cloud-based platforms where it’s stored, analyzed, and turned into insights - accessible from anywhere at any time.

This connectivity brings two major advantages.

First, data from different assets can be aggregated. Teams can monitor multiple machines across different locations on a single dashboard to compare trends and prioritize interventions across the entire operation. This is a major advantage over dealing with issues one asset at a time.

Second, analysis becomes smarter. Cloud platforms leverage AI and machine learning to process large volumes of data and detect patterns that would take human analysts hours or days to catch.

These systems can spot subtle shifts that indicate early failure, provide prescriptive recommendations, and even adapt over time to the behavior of each specific asset.

This level of automation and visibility is especially critical in industries dealing with labor shortages or distributed operations.

It allows fewer technicians to monitor more assets, respond faster, and make better decisions without being physically present.

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Types of Condition Monitoring

Condition monitoring isn’t one-size-fits-all. Different assets, failure modes, and operational conditions require different techniques. 

The key is to choose the method or combination that best suits the type of equipment and the issues being tracked.

Here are the most common types:

Vibration Monitoring

Vibration monitoring is one of the most critical and widely used techniques in industrial maintenance, especially for rotating machinery.

That’s because vibration often tells what’s going wrong inside a component long before a failure occurs.

The principle is straightforward. Every machine has a baseline vibration signature when it's operating normally. As components start to wear or loosen, that vibration pattern changes. 

With high-frequency sensors in place, these deviations can be detected in real time.

This technique is particularly effective at identifying:

• Imbalances in rotating components
• Misalignments in shafts and couplings
• Bearing wear or failure
• Looseness in structural or mechanical elements

By analyzing vibration amplitude and frequency, technicians can pinpoint not only the presence of a problem but often the exact type and location of the fault. 

For example, a high-frequency spike could signal bearing wear, while a low-frequency, repeating pattern might suggest imbalance.

Infrared Thermography

Infrared thermography uses thermal imaging to detect abnormal temperature patterns in equipment, making it a powerful, non-invasive method for spotting developing issues early.

Every machine emits heat during operation, and when components start to fail, that heat profile changes. This can happen due to friction, overload, poor lubrication, or electrical faults.

Whatever the cause, thermographic cameras capture this data and turn it into visual heat maps, allowing technicians to quickly identify hotspots and temperature deviations.

This technique is especially effective for:

• Detecting loose or overloaded electrical connections
• Identifying misalignments or worn bearings
• Spotting insulation failures in electrical panels
• Locating fluid or gas leaks in pressurized systems

Because thermography can be performed while equipment is running, it’s ideal for inspecting critical systems without interrupting operations. That includes high-voltage areas where traditional inspection methods may pose safety risks.

Thermal scans are fast, safe, and repeatable, making them perfect for routine inspections of large numbers of assets. 

When integrated into a condition monitoring program, they help ensure that minor thermal anomalies are caught before they escalate into critical failures.

Oil Analysis

Oil analysis is one of the most insightful techniques for evaluating internal wear and contamination inside mechanical systems. It can be done without disassembling a single part.

Over time, as machines operate, their lubricants collect trace elements: metal particles from wear, byproducts of combustion, or external contaminants like water or dust. 

Analyzing these lubricants can reveal a lot about the condition of the components inside.

Typical oil analysis focuses on:

• Wear metals: Indicates which internal parts are deteriorating (e.g., iron from gears, copper from bearings)
• Contaminants: Detects intrusion of water, fuel, or coolant
• Additive levels: Monitors if the lubricant’s protective properties are degrading
• Viscosity and acidity: Helps determine if the oil is still doing its job effectively

For example, elevated silicon might suggest external dirt ingress, while high iron paired with copper could point to simultaneous gear and bearing degradation.

This technique is especially critical for systems with large oil volumes, such as gearboxes, compressors, engines, and hydraulic circuits, where lubricant health directly impacts performance.

Ultrasonic Analysis

Ultrasonic analysis leverages high-frequency sound waves—often far beyond the range of human hearing—to detect subtle mechanical or fluid-related issues before they become critical.

They produce high-frequency acoustic signals when friction increases, pressure leaks occur, or electrical discharges happen. 

Ultrasonic sensors pick up these signals and translate them into actionable data through contact (structure-borne) or non-contact (airborne) methods.

This technique excels at identifying:

• Pressure or vacuum leaks in compressed air and gas systems
• Early-stage bearing failure
• Poor lubrication conditions
• Steam trap malfunctions
• Electrical arcing or corona discharges

Compared to other methods, ultrasound is particularly sensitive to the first signs of failure. 

For instance, a bearing in the earliest phase of wear may still appear normal under vibration analysis but will emit ultrasonic noise due to microscopic friction increases.

Ultrasonic tools are essential in many routine inspections because they can isolate and pinpoint sound signatures even in noisy environments. 

It’s fast, safe, and doesn’t require disassembly, which is critical for hard-to-reach or enclosed systems.

Ultrasound can also be used proactively to verify lubrication quality. When used this way, it helps eliminate over- and under-lubrication, extending component life and reducing risk.

Acoustic Emission Testing

Acoustic emission testing (AET) is a high-sensitivity method that listens for stress waves released when materials deform, crack, or experience sudden structural changes.

Unlike general ultrasonic analysis, which detects surface-level anomalies, AET focuses on transient elastic waves generated inside the material itself.

These emissions are the result of real-time energy releases, such as a crack propagating or a fiber breaking, which makes AET ideal for detecting critical flaws while they’re forming.

This technique is particularly suited for:

• Crack growth monitoring in pressure vessels or structural components
• Early detection of bearing fatigue in high-speed equipment
• Identifying faults in rotating components under heavy load
• Monitoring weld integrity and material degradation in critical assets

Sensors are mounted directly onto the equipment surface, often in arrays, to triangulate the origin of emissions. This tactic allows the sensors to detect the problem and its precise location.

Real-time detection is vital for industries requiring continuous mission-critical systems monitoring, such as power generation, oil & gas, or aerospace manufacturing.

While it requires skilled data interpretation, acoustic emission testing can uncover issues that other methods miss, giving maintenance teams a chance to respond to internal damage as soon as possible.

Electromagnetic Monitoring

Electromagnetic monitoring is a technique that uses magnetic field behavior to detect faults in metallic components, especially where cracking, corrosion, or material fatigue may be developing beneath the surface.

The concept is based on how electromagnetic fields interact with conductive materials. When a component is intact, the field flows uniformly. But when there's a discontinuity—like a crack, deformation, or corrosion pocket—the field is disrupted. 

These changes can be measured with sensors, revealing internal or surface-level defects.

Common techniques under this umbrella include:

• Magnetic Flux Leakage (MFL): Used to find corrosion and pitting in pipelines and storage tanks
• Eddy Current Testing: Ideal for detecting surface and sub-surface flaws in conductive materials
• Alternating Current Field Measurement (ACFM): Effective in harsh environments, such as offshore structures
• Metal Magnetic Memory (MMM): Useful for monitoring stress concentration zones in welds and pressure vessels

Another key advantage is that many of these methods don’t require direct contact or surface prep, allowing for faster inspection with minimal interruption to operations.

Electromagnetic monitoring is particularly valuable for components that operate under cyclic loads or in corrosive environments, like turbines, heat exchangers, or structural supports. 

Laser Interferometry

Laser interferometry is a precision monitoring technique that detects extremely small movements, surface deformations, or structural changes by measuring how laser light waves interfere with one another.

It uses a laser beam split into two paths—one directed at the measured object and the other as a reference. When the reflected beams are recombined, any displacement or distortion in the object shifts the interference pattern.

This shift can be measured with incredible accuracy, often down to the nanometer scale. This level of sensitivity makes laser interferometry ideal for aerospace systems, turbine blades, and high-speed machining centers.

It’s particularly effective for:

• Monitoring structural deflections in rotating or high-speed equipment
• Detecting thermal expansion or stress in machinery components
• Identifying cracks or delamination in composite materials
• Measuring minute misalignments in precision assemblies

What sets it apart is the ability to assess not just motion, but how that motion deviates from expected behavior over time. 

This makes it an advanced tool not only for failure detection but also for quality control and performance benchmarking.

Motor Circuit Analysis (MCA)

Motor circuit analysis (MCA) is a diagnostic technique used to assess the health of electric motors by evaluating the integrity of their electrical circuits without running the motor during testing.

MCA works by applying a low-voltage signal to the motor’s windings and analyzing the response. 

This allows maintenance teams to detect problems in the stator, rotor, insulation, and connections before those issues cause failure during operation.

This method is ideal for identifying:

• Insulation degradation
• Open or short circuits in motor windings
• Rotor bar issues
• Loose or corroded connections
• Imbalances across phases

One of its biggest advantages is that it can be used as both an online and offline test. 

Offline MCA doesn't require the motor to be energized, making it safer and more flexible for use during planned downtime or before startup after storage or repair.

It’s especially useful in facilities where electric motors power critical assets like pumps, conveyors, HVAC systems, and more.

Wired vs Wireless Condition Monitoring System

Choosing between a wired and wireless condition monitoring system comes down to a few key factors: asset criticality, environmental conditions, data requirements, and scalability.

Wired Condition Monitoring System

Wired systems are known for their stability and reliability. 

They offer continuous power and high data transmission rates, making them a solid choice for high-speed machinery or environments with heavy interference,  near variable frequency drives (VFDs), or high-voltage equipment.

They’re commonly used on critical assets where real-time, high-resolution data is essential and where physical cabling isn’t a constraint.

However, installation is more complex and costly. Running cable across a large facility can take significant time and planning, especially in hard-to-reach or hazardous areas.

Once installed, these systems are harder to modify or scale.

Wireless Condition Monitoring System

On the other hand, wireless systems are flexible, cost-effective, and fast to deploy. 

They’re ideal for plants that want to scale condition monitoring across many assets, especially those spread across multiple floors or buildings. 

Wireless sensors operate on battery power and transmit data using secure protocols like Bluetooth Low Energy (BLE), Wi-Fi, or proprietary mesh networks.

While earlier versions raised concerns about reliability or latency, today’s systems have closed that gap significantly, making wireless monitoring suitable even for medium- to high-priority assets.

Hybrid Condition Monitoring System

In most facilities, the optimal setup is a hybrid system: wired sensors on high-value, always-on assets and wireless sensors on less critical or mobile equipment. 

This approach ensures full coverage while balancing installation effort and budget.

Ultimately, it’s not about choosing one over the other. The challenge is matching the right system to the right asset so teams get the data they need without overcomplicating deployment.

Benefits of Condition Monitoring

By tracking asset health in real time, condition monitoring transforms the way teams manage downtime.

Here’s how it delivers impact where it counts:

Avoid Unplanned Downtime

Unplanned stops are among the most expensive and disruptive events in any industrial operation. 

Condition monitoring helps prevent these by detecting the earliest signs of failure, allowing teams to schedule repairs before breakdowns occur.

Instead of reacting to emergencies, maintenance can plan around production schedules. 

As a result, they keep operations running smoothly and avoid the domino effect of delayed deliveries, missed targets, and strained crews.

Protect Your Other Assets

Failures don’t always stay isolated. When a motor seizes, it can strain a connected gearbox, or a worn bearing can misalign an entire shaft assembly. 

Condition monitoring limits this damage by catching issues before they create a chain reaction.

By isolating the root cause early, teams prevent collateral damage and reduce the risk of multiple asset failures from a single overlooked fault.

Eliminate Unnecessary Maintenance to Maximize ROI

Not every machine needs maintenance on a fixed schedule. Yet many teams still over-service equipment out of caution, wasting time, parts, and labor.

Condition monitoring helps break that cycle. It shows which machines are running fine and which ones need attention. 

When maintenance happens only when it’s truly needed, labor efficiency and asset ROI improve.

More Efficient Maintenance

Knowing what’s wrong before opening the panel changes everything. 

With condition monitoring, technicians don’t need to spend hours diagnosing a fault, they’re already walking in with data.

That clarity speeds up work orders, improves task prioritization, and helps teams focus their energy where it matters. It's smarter maintenance with fewer blind spots.

Improved Safety

Unexpected failures are costly and dangerous. A catastrophic mechanical breakdown can expose technicians to high heat, moving parts, or live electrical systems.

Condition monitoring helps reduce these risks by minimizing reactive interventions. 

It also enables safer inspection routines, allowing remote monitoring of high-risk assets without direct contact.

Improved Asset Efficiencies

With real-time data guiding decisions, operations can maintain optimal load levels, detect inefficiencies, and eliminate energy waste tied to failing components.

Over time, this leads to higher equipment availability, better production throughput, and reduced operational drag, without the need to invest in new equipment.

Challenges of Condition Monitoring

While condition monitoring brings clear advantages, implementing it isn’t without its hurdles. 

From cost concerns to scheduling uncertainties, every organization must navigate trade-offs when developing or expanding a monitoring program.

Here’s what often stands in the way and how to plan around it:

Installation Costs

Getting started can require upfront investment, especially when deploying sensors across a large asset base. 

Equipment might need retrofitting, and the installation itself may involve downtime, integration with existing systems, or layout modifications.

For some teams, this raises questions of priority: which assets are worth monitoring now, and which can wait? 

This is where clear asset hierarchies are critical. Focusing on high-risk, high-value assets first helps justify costs while delivering early ROI.

Operational Costs

Once the system is in place, there’s still the matter of ongoing expenses. 

Wireless sensors need battery replacements, software platforms may have licensing fees, and data management requires time and attention.

If teams aren’t set up to handle the influx of data or if there’s no one analyzing the results, the system risks becoming noise instead of insight. 

That’s why it’s important to ensure the tech has a detailed, precise process to follow. For example, who’s watching the data, how often, and what happens when something triggers an alert?

Unpredictable Maintenance Scheduling

While predictive strategies offer flexibility, they also move away from traditional routines. 

Some teams prefer fixed intervals because they’re easy to plan and budget on a first impression. Condition-based schedules can feel less predictable—even if they’re more efficient.

The reality is that this uncertainty can be managed. With historical data, alert thresholds, and failure models in place, teams can still accurately forecast workloads and parts usage. 

However, it does require a shift in mindset from scheduling based on time to scheduling based on need.

How to Build a Condition Monitoring Program

Condition monitoring requires a step-by-step process that blends hardware, data, and team workflow.

Here’s how to structure a program that actually works on the plant floor:

Step 1: Collect Historical Data

Before adding any sensors, it’s important to understand the past behavior of the monitored assets. 

Historical data sets the foundation, so gather maintenance logs, failure reports, runtime hours, and repair history.

This data helps prioritize which assets should be monitored first and gives context to what "normal" and "abnormal" look like. 

If vibration levels spiked before a bearing failure last year, that insight is valuable when setting thresholds later.

Step 2: Install the Sensors

Once priorities are clear, it’s time to install the condition monitoring hardware. The right sensor depends on the asset type and failure mode. For example:

• Vibration sensors for rotating machinery
• Ultrasonic sensors for compressed air systems
• Temperature sensors for motors and bearings
• Oil analysis for gearboxes and hydraulic equipment

Installation may involve retrofitting or mounting in tight spaces, so planning ahead is critical. Safety, signal range, and accessibility all play a role.

Step 3: Establish Your Baselines

Data collection begins as soon as sensors are online. But it takes time to understand what "normal" looks like. 

That’s where baselining comes in.

During this phase, assets are monitored under regular operating conditions to define healthy behavior. 

These baselines become reference points. Any significant deviation from them is what triggers alerts later.

Step 4: Monitor Your Assets

With baselines in place, the system shifts into continuous monitoring. Real-time data flows to a central platform, the software flags anomalies, and maintenance teams get alerts when something shifts.

The program becomes a living, learning system that refines itself over time.

From here, teams can move into proactive planning, smart task scheduling, and even integrate condition monitoring data into existing CMMS or ERP platforms for seamless operations.

Bring Your Condition Monitoring Strategy to Life with Tractian

A strong condition monitoring strategy should detect failures and create the conditions that avoid them entirely.

With the right setup in place, maintenance teams can move from reactive firefighting to confident, data-driven planning. 

That means fewer surprises, more efficient repairs, and extended equipment life across the board.

The key is building a system that combines real-time visibility, automated insights, and ease of use. 

A system that scales across asset types, connects to existing workflows, and helps prioritize actions based on actual risk—not relying on run hours or guesswork.

Having trouble tracking your assets? Tractian combines real-time insights with AI-powered diagnostics to simplify condition monitoring from start to finish.