Introduction: The Reliability Challenge in Data-Rich Assets
Modern industrial equipment—ranging from rotating machinery and thermal systems to mobile production units and heavy process assets—generates vast volumes of sensor data. Pressures, temperatures, flows, speeds, vibrations, and electrical signals are continuously recorded at high frequency.
Yet despite this abundance of data, many organizations still rely on scheduled maintenance or reactive repairs, often discovering degradation only after performance loss or failure. The core challenge is not data availability, but the lack of a structured methodology to transform raw time-series data into actionable reliability insights.
This article presents a generalized Prognostics and Health Management (PHM) framework designed for data-intensive industrial equipment, where assets operate under varying loads, modes, and operational stages. The framework focuses on scalability, interpretability, and operational usability, enabling reliability and maintenance teams to move from data monitoring to condition-based decision-making.
A Framework-Driven Approach to PHM
Rather than treating PHM as a collection of isolated models, the proposed approach views PHM as a systematic pipeline with five tightly connected layers:
- Data Acquisition and Platform-Agnostic Enablement
- Data preprocessing and normalization
- Operational contextualization through tagging
- Condition monitoring using deviation-based models
- Maintenance decision integration and feedback
This structure allows organizations to scale PHM across multiple subsystems, multiple asset types, and geographically distributed fleets.
1. Data Acquisition and Platform-Agnostic Enablement
Most industrial assets today are equipped with embedded sensors and control systems that continuously record operational parameters. These data streams are typically:
- High-frequency time series
- Multi-dimensional (often hundreds of channels per asset)
- Collected across intermittent operating cycles
A key enabler of scalable PHM is centralized data availability, rather than any specific deployment technology. Asset data may be aggregated through cloud-based platforms, edge computing systems, on-premise historians, or periodic offline data transfers, depending on operational constraints, cybersecurity policies, and cost considerations.
In many environments, data is transmitted continuously or in scheduled batches to a central analytics environment. In others—such as remote, air-gapped, or cost-constrained operations—data can be periodically extracted from equipment controllers or local systems and processed on a workstation or local server on a daily or weekly basis.
Regardless of deployment model, centralized data access enables:
- Consistent processing across assets and fleets
- Integration with analytics and visualization tools
- Long-term historical analysis for trend and degradation assessment
Importantly, the framework does not require real-time streaming to be effective. Post-operation or near-real-time ingestion is often sufficient to support reliable condition-based maintenance, while significantly reducing infrastructure complexity and deployment cost.
2. Data Preprocessing: Preparing Time-Series Data for Analysis
Raw industrial sensor data is rarely analysis-ready. Noise, missing values, unit inconsistencies, and mixed operating conditions can obscure true equipment behavior.
A robust preprocessing layer is essential and typically includes:
- Partitioning continuous time series into logical segments (e.g., jobs, cycles, runs)
- Outlier removal to eliminate sensor faults and spurious spikes
- Standardization of units of measurement to enable cross-asset comparison
- Interpolation of missing values, using context-appropriate methods (e.g., forward-fill, linear interpolation)
This step transforms raw telemetry into a clean, consistent dataset that preserves physical meaning while reducing analytical noise.
3. Operational Tagging: Contextualizing Equipment Behavior
One of the most overlooked challenges in PHM is context. The same sensor value can represent normal behavior in one operating stage and abnormal behavior in another.
To address this, the framework introduces operation tagging—a structured method to classify time-series data into distinct operational modes or stages, such as:
- Idle vs. loaded operation
- Start-up, steady-state, or transient phases
- Process-specific stages (e.g., mixing, pumping, cooling, pressurizing)
Operational tagging can be implemented using a machine-learning-assisted approach, trained on a limited set of manually labeled examples and refined through subject-matter expertise. Once established, tagging enables:
- Automatic identification of regions of interest
- Reusable data partitions for multiple models
- Consistent comparison across assets and time periods
This layer is a force multiplier: one well-designed tagging scheme can support dozens of downstream PHM models across subsystems.
4. Deviation-Based Condition Monitoring Using RMSE Metrics
With data contextualized by operation, the framework applies deviation-based condition monitoring rather than failure classification. This is particularly valuable in industrial environments where labeled failure data is scarce.
Modeling Philosophy
Instead of predicting failures directly, models learn the expected behavior of healthy equipment under specific operating conditions. Deviations from this expected behavior are treated as indicators of degradation.
A commonly effective approach is regression-based modeling, where:
- One or more measured parameters are predicted from related operating variables
- The Root Mean Square Error (RMSE) between predicted and actual values is calculated
- RMSE distributions from historical healthy data define normal operating envelopes
Threshold Definition
Rather than arbitrary limits, thresholds are derived statistically—for example, using the lower-percentile distribution of RMSE values from healthy operating data. This allows:
- Asset-specific sensitivity tuning
- Robust handling of process variability
- Transparent interpretation by reliability engineers
This methodology is applicable to a wide range of subsystems, including pumps, cooling systems, fluid circuits, thermal loops, and rotating components.
5. Alarm Generation and Maintenance Decision Support
When deviation thresholds are exceeded, the framework generates condition-based alerts, not failure alarms. These alerts indicate that equipment behavior has moved outside its statistically defined healthy envelope.
Key design principles for alerting include:
- Alerts are reviewed by reliability or maintenance engineers, not acted upon blindly
- Contextual dashboards visualize deviation trends and operating stages
- Alerts are integrated into existing maintenance management systems (CMMS)
This ensures that PHM supports—not replaces—engineering judgment.
6. Closing the Loop: Feedback and Continuous Improvement
A defining feature of the framework is its closed-loop design. Maintenance findings and corrective actions are fed back into the PHM system to:
- Refine thresholds
- Improve tagging accuracy
- Enhance model robustness
Over time, this feedback loop increases confidence in data-driven maintenance decisions and enables gradual expansion to additional subsystems and asset classes.
Why This Framework Scales Across Industries
Although originally developed in a complex industrial environment, the framework is industry-agnostic and applicable wherever:
- Assets generate large volumes of time-series data
- Operating conditions vary significantly
- Failures are costly, but labeled failure data is limited
Industries such as manufacturing, energy, transportation, mining, chemicals, and heavy equipment operations can adopt this approach to move beyond reactive maintenance and toward data-driven reliability management.
Conclusion: Turning Data into Reliable Decisions
The true value of PHM lies not in sophisticated algorithms alone, but in structured integration—from data ingestion to operational context, from statistical modeling to maintenance action.
By combining preprocessing, operation tagging, deviation-based models, and cloud-enabled monitoring, organizations can transform raw sensor data into clear, actionable insights that extend asset life, reduce unplanned downtime, and improve operational reliability.
As industrial assets continue to become more instrumented, frameworks like this will be essential for turning data abundance into engineering confidence.
