What is Condition Monitoring? Introducing Yokogawa's Solutions

What is Condition Monitoring?

 

Definition

Condition monitoring is a technology for continuously acquiring data such as temperature, vibration, sound, electrical current, and oil composition while machinery and equipment remain in operation, enabling early detection of abnormalities and signs of deterioration.

Unlike traditional scheduled inspections or corrective maintenance, condition monitoring allows real-time assessment of operating equipment health, preventing unexpected downtime and enabling planned maintenance interventions.

 

Purpose — Why is it Necessary?

The purpose of condition monitoring is to achieve stable equipment operation and cost optimization.
The main benefits include:

• Early detection of failure precursors to avoid unplanned stoppages
• Optimization of maintenance timing to reduce unnecessary replacements and inspections
• Enhanced safety through mitigation of accident and damage risks
• Extension of machinery lifespan to improve return on equipment investment
• Data-driven decision-making to reduce reliance on individual technician expertise

 

The Condition Monitoring Process

Condition monitoring consists of a continuous cycle of observation, recording, analysis, and response —not simply sensor installation.

 

Sensor Placement

 

Purpose:

Position sensors where abnormalities manifest most clearly, minimizing noise interference while ensuring reliable data acquisition.

Representative Sensor Types:

• Accelerometers (vibration) 　
• Temperature sensors (thermal conditions)
• Current sensors (electrical load)
• Pressure sensors (fluid systems)
• Oil analysis sensors (lubrication condition)

Key Design Considerations:

• Target Measurement Characteristics: For motors, focus on bearings; for pumps, on casings or discharge points; for gears, near meshing areas.
• Signal Transmission Path: Select positions where vibration and heat traveling through metal structures experience minimal attenuation.
• Mounting Method: Options include magnetic mounting, bolt mounting, and adhesive bonding. Poor mounting precision increases signal noise.
• Maintainability: Consider ease of inspection and replacement, cable routing, and environmental protection (heat/water resistance).

Design decisions at this stage significantly influence subsequent analysis accuracy and trouble detection speed.

 

Data Acquisition

This process collects and converts signals output from installed sensors, transmitting them to recording devices or cloud platforms.

Sampling Frequency:

The rate at which signals are measured at regular intervals. High-frequency phenomena such as vibration and acoustic signals require sampling rates of 1kHz to 50kHz or higher.

A/D Conversion (Analog to Digital Conversion):

The process of converting analog signals (voltage changes) into digital values. Insufficient conversion precision prevents detection of subtle changes.

Data Collection Methods:

• Online Monitoring: Continuous real-time data acquisition, suitable for remote monitoring and automatic alerting.
• Offline Monitoring: Periodic connection of measuring instruments for data recording, reducing implementation costs.

Communication Interfaces:

Depending on the facility environment, either wired connections (e.g., Ethernet, RS-485) or wireless technologies (e.g., Wi-Fi, LoRa, 5G) can be selected.

For integrated facility-wide monitoring, systems typically interface with PLCs (Programmable Logic Controllers) or SCADA (Supervisory Control and Data Acquisition) systems.

 

Data Analysis

Trend Analysis:

Tracking parameter changes over time to detect gradual deviations from baseline.

• Example: Temperature rising 2°C monthly suggests declining cooling performance.

Spectral Analysis (FFT: Fast Fourier Transform):

Decomposing vibration data into frequency components to identify dominant vibration frequencies. Specific frequency peaks indicate characteristic abnormal patterns such as shaft imbalance or gear damage.

Statistical & AI Analysis:

Employing statistical methods (mean, variance, outlier detection) and machine learning models for anomaly detection. AI systems learn from historical normal operating data to automatically detect subtle pattern deviations.

Visualization:

Presenting analysis results through graphs, heat maps, and 3D models to enable intuitive understanding of equipment conditions by field personnel.

 

Alert Generation

Threshold Configuration:

Establishing criteria levels such as "caution," "warning," and "critical" for each measurement parameter.

• Example: Vibration acceleration of 10mm/s² triggers caution level, 20mm/s² triggers warning, 30mm/s² triggers critical shutdown protocol.

Alert Notification Methods:

• On-site monitors or warning light activation
• Email/SMS notification
• Automatic alerts on IoT dashboards
• Automated ticket generation in maintenance management systems

False Positive Mitigation:

To reduce incorrect detections, multi-parameter analysis combining multiple indicators for comprehensive assessment has become standard practice.

 

Maintenance

Predictive Maintenance:

Conducting planned component replacement when abnormal indicators appear. This approach maximizes component lifespan utilization while reducing costs.

Condition-Based Maintenance (CBM):

Maintenance determined by actual equipment condition rather than fixed time intervals. This prevents excessive inspections by basing decisions on measured deterioration levels.

Maintenance Record Management:

Documenting work history and replacement records for use in future maintenance planning and analysis model refinement. Accumulated data can serve as training datasets for enhanced anomaly detection algorithms.

 

Process Integration

The condition monitoring process consists of three integrated stages:

1. Sensor Placement → Data Acquisition: Converting physical phenomena into quantifiable data
2. Data Analysis → Alert Generation: Detecting and evaluating abnormal conditions
3. Maintenance: Implementing corrective actions and establishing preventive measures

This integrated approach enables the transition from reactive maintenance to predictive maintenance strategies. Condition monitoring functions as a comprehensive system connecting equipment, data, and maintenance personnel.

 

Representative Condition Monitoring Technologies

Condition monitoring employs diverse technologies.
The following five methods represent the most widely adopted approaches in industrial applications. Understanding each measurement principle and application area enables appropriate technology selection for specific equipment types.

 

Vibration Analysis

Purpose

Identifying shaft imbalance, misalignment, bearing wear, gear damage, and similar mechanical issues through characteristic vibration patterns.

Analysis Methods

• Time Domain Analysis: Examining vibration waveform changes over time to identify periodicity and transient impacts.
• Frequency Domain Analysis (FFT): Decomposing vibration signals by frequency to identify dominant components. Each abnormality type exhibits a unique frequency signature.
• Envelope Analysis: Extracting minute impact components to detect incipient bearing damage.

Advantages

• Enables early diagnosis by capturing abnormality indicators before significant progression
• Advanced automation with AI-based failure prediction approaching practical deployment

Limitations

• Sensitive to mechanical structure and installation environment; requires effective noise filtering and precise sensor placement

 

Infrared Thermography

Purpose

Detecting abnormalities manifesting as temperature anomalies, including overheating, poor electrical contact, insulation deterioration, and increased friction.

Application Examples

• Elevated contact resistance in electrical panels, breakers, and distribution equipment
• Motor bearing overheating
• Pipe and valve blockages or leaks
• Building envelope and insulation material deterioration

Advantages

• Non-contact measurement enables safe inspection of energized equipment
• Wide-area scanning capability with intuitive image-based interpretation

Limitations

• Limited to surface temperature measurement; unsuitable for internal component diagnosis
• Susceptible to environmental factors including reflective surfaces and air movement

 

Ultrasonic & Acoustic Monitoring

Purpose

• Early detection of incipient bearing and gear damage
• Identification of minute leaks in compressed air, gas, and vacuum systems
• Diagnosis of steam trap operational failures
• Detection of partial discharge in electrical equipment

Advantages

• Detects early-stage abnormalities difficult to identify through vibration or thermal monitoring
• Compact, portable instrumentation suitable for routine inspection protocols
• Directional sensing enables precise abnormality localization

Limitations

• High-frequency signals attenuate rapidly; detection range decreases with distance
• High ambient noise environments require sophisticated filtering and signal processing

 

Oil Analysis

Purpose

• Monitoring wear progression in gears, bearings, and piston assemblies
• Tracking oil degradation, oxidation, and additive depletion
• Early detection of declining lubrication performance and contamination

Key Analysis Parameters

• Metal Element Analysis: Identifying wear sources through detection of iron, copper, chromium, aluminum, and other metallic particles
• Viscosity Measurement: Assessing lubrication effectiveness and thermal conductivity
• Water Content Measurement: Detecting condensation and coolant contamination
• Total Acid Number (TAN) & Total Base Number (TBN): Quantifying oxidation and degradation levels

Advantages

• Enables condition assessment of sealed equipment without disassembly
• Regular sampling supports comprehensive long-term trend analysis

Limitations

• Sampling methodology errors can compromise result accuracy
• Result interpretation requires specialized technical knowledge

 

Motor Circuit Analysis (MCSA)

Purpose

• Detection of winding faults including short circuits, open circuits, and insulation breakdown
• Phase balance verification (inter-phase current symmetry)
• Identification of mechanical issues such as bearing defects and rotor bar damage
• Power quality assessment (voltage distortion, harmonic content)

Analysis Techniques

• Motor Current Signature Analysis (MCSA): Analyzing harmonic and sideband components in current waveforms to identify specific fault signatures
• Impedance Measurement: Evaluating winding condition through resistance and reactance measurements per phase
• Startup Transient Analysis: Examining motor starting current profiles to identify abnormal loading conditions

Advantages

• Online diagnostic capability eliminates production downtime
• Leverages existing power monitoring infrastructure, minimizing additional sensor requirements
• High compatibility with AI analytics and cloud-based monitoring platforms

Limitations

• Susceptible to electrical noise interference in industrial environments
• Accurate diagnosis requires detailed motor nameplate data (rated current, pole count, etc.)

 

Technology Selection Guidelines

Optimal monitoring approaches vary based on equipment type and operating environment:

• Rotating machinery production lines: Combined vibration analysis and motor circuit analysis
• High-temperature, high-voltage equipment: Infrared thermography
• Fluid and pneumatic systems: Ultrasonic leak detection
• Heavy-duty machinery and process equipment: Integrated oil analysis and vibration monitoring

Single-technology approaches often fail to detect all potential failure modes. Consequently, integrated multi-technology condition monitoring strategies have become industry best practice.

 

Introducing Yokogawa's Solutions

 

In addition to monitoring equipment conditions to understand their operational health, it has become increasingly important in recent years to leverage collected data to enhance maintenance practices and improve overall operational reliability.

By combining advanced sensing technologies, data analytics platforms, and optimization approaches for both operations and maintenance, Yokogawa helps customers increase the value of their assets throughout the plant lifecycle. Yokogawa’s concept of Intelligent Maintenance provides a structured framework that integrates condition monitoring data, operational data, and maintenance information to continuously improve equipment and operational reliability.

Building on this foundation, Yokogawa extends Intelligent Maintenance into the era of agentic AI through its Multi-Agent Generative Systems (MAGS). MAGS are governed teams of cognitive AI agents purpose-built for complex industrial environments. They preserve expert knowledge, manage operational complexity at machine speed, and deliver explainable, trustworthy decisions at scale. Rather than relying solely on dashboards or isolated analytics, MAGS enable organizations to move safely toward governed autonomy—where insights are not only generated but acted upon within clearly defined operational and governance boundaries.

For customers exploring the next step beyond traditional condition monitoring—such as predictive insights, maintenance optimization, higher level reliability management and safe agent operations at scale—this approach offers a powerful and future oriented pathway.

For more information, please visit:
Enhancing Asset and Operations Reliability with Intelligent Maintenance

 

Conclusion

Condition monitoring provides real-time visibility into equipment operational status. By integrating multiple data sources—vibration, temperature, acoustic signatures, electrical parameters, and lubricant condition—organizations can prevent catastrophic failures while maintaining safe and efficient operations.

While implementation requires capital investment and technical expertise, properly executed condition monitoring programs deliver measurable returns through maintenance cost reduction, improved equipment availability, and extended asset lifespan.