Predictive Maintenance (PdM) – Condition‑Based Reliability for GxP Assets

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • Predictive Maintenance, PdM, TPM, OEE, CMMS • Pharma, Biotech, Devices, Food

Predictive Maintenance (PdM) is a condition‑based maintenance strategy that uses real‑time and historical data to estimate asset health and predict failures before they occur. Instead of changing parts strictly by calendar or run‑hours—or waiting until something breaks—PdM combines sensors, diagnostics and analytics so that maintenance is performed when risk actually warrants it. In regulated manufacturing, the goal is not just fewer breakdowns, but more reliable control of processes that protect quality, safety and compliance.

“We stopped fixing things when they failed. Now we fix them when the data shows they’re about to.”

1) What Predictive Maintenance Is – and How It Differs from Preventive Maintenance

Traditional preventive maintenance (PM) uses fixed intervals: change a seal every six months, overhaul a pump every 4 000 hours, recalibrate an instrument annually. Corrective maintenance reacts after the fact when something fails. Predictive Maintenance (PdM) adds a third mode: monitor condition and operating context continuously (or frequently) to determine when a component is actually degrading, then schedule an intervention just in time to avoid failure.

Practical PdM can be simple or sophisticated. At the basic end, it may be alarmed thresholds on bearing temperature or vibration. At the advanced end, it uses multivariate models or machine‑learning classifiers trained on historian data. Either way, the defining feature is that maintenance timing is driven by asset health indicators, not only by the calendar. In regulated plants, PdM is usually layered on top of a baseline PM and calibration programme, not used to eliminate it entirely without strong justification and regulatory alignment.

2) Regulatory Context – Maintenance as Part of the Control Strategy

GxP regulations expect equipment to be designed, qualified, maintained and calibrated so that it consistently performs as intended. Concepts such as equipment qualification (IQ/OQ/PQ), utilities qualification (UQ) and calibration status all depend on reliable maintenance. Regulators rarely use the term “predictive maintenance”, but they scrutinise how maintenance decisions are made, documented and justified—especially for critical utilities, sterile barriers, environmental controls and process equipment that directly affect CQAs.

When PdM influences maintenance intervals or decisions for GxP‑critical assets, it becomes part of the control strategy and must be covered by QRM, validation and SOPs. That includes documenting monitored parameters, analytical methods, alarm logic, decision thresholds and how PdM outputs translate into work orders in the CMMS. If PdM recommendations allow extension of an interval or deferral of a task, the rationale must be traceable, risk‑based and supported by data, not simply by an algorithm’s suggestion.

3) Data Sources for PdM – Sensors, Historians and Context

Effective PdM relies on combining multiple data sources into a coherent picture of asset health. Typical inputs include basic process signals (flows, pressures, temperatures), dedicated condition‑monitoring sensors (vibration, acoustic, motor current, oil analysis), laboratory results, environmental conditions and operator observations. Modern PdM programmes increasingly tap into the manufacturing data historian and MES to capture long‑term trends, operating modes and duty cycles.

Context is as important as raw numbers. A pump motor running hot during CIP may be normal; the same trend at low load could be a warning sign. PdM architectures therefore often include business context from the MES (batch step, product, cleaning phase) and CMMS (age since overhaul, known issues) alongside sensor data. Well‑designed PdM avoids single‑signal “false positives” by combining multiple indicators and looking for consistent patterns of degradation rather than isolated spikes.

4) PdM, CMMS and Asset Master Data

PdM does not replace the Computerized Maintenance Management System (CMMS)—it makes it smarter. The CMMS remains the system of record for asset registers, maintenance plans, work orders, spare parts and technician activity. PdM engines, dashboards or analytics tools feed insights into the CMMS in the form of risk‑prioritised recommendations: “inspect this bearing within 7 days”, “schedule lubrication at next planned stop”, “plan a seal replacement at the upcoming shutdown”.

To make that work in a GxP context, asset master data must be in good shape: criticality ratings, links to qualified states, calibration status, equipment hierarchies and relationships to processes and utilities. PdM logic and models should reference controlled asset identifiers, not ad‑hoc tags. Change control for PdM rules—who can alter thresholds, data mappings or failure classifications—typically resides in the same QMS framework that governs maintenance plans and equipment records.

5) Analytics Approaches – From Rules to Machine Learning

PdM analytics range from straightforward condition rules to advanced machine learning. At the simpler end are engineered thresholds, rate‑of‑change limits and pattern rules backed by vendor manuals and engineering judgement. These can be implemented quickly and are often easiest to justify in a GxP environment because their logic is transparent and directly tied to known failure modes.

More advanced approaches use multivariate statistics, SPC‑style control charts and anomaly‑detection or classification algorithms trained on historical data. Where machine‑learning models are used, their lifecycle must align with CSV and the site’s VMP: training data documented, performance characterised, intended use defined, retraining managed under change control. In practice, many regulated plants start with transparent methods and add more sophisticated models where the risk and benefit case is strong.

6) PdM, TPM and OEE

PdM fits naturally under the broader umbrella of Total Productive Maintenance (TPM), which emphasises operator ownership, autonomous maintenance and reliability culture. Rather than replacing TPM, PdM provides a more precise way to identify which assets need attention and when, enabling frontline teams to focus on high‑risk issues instead of routine checks that rarely find problems.

From a performance perspective, PdM directly influences Overall Equipment Effectiveness (OEE). By reducing unplanned downtime and quality‑related losses tied to equipment degradation, PdM improves the “Availability” and “Quality” components of OEE. Over time, data from PdM events and OEE losses should feed back into design, procurement and process‑improvement decisions, creating a loop where reliability engineering and operations share a common view of where to invest maintenance effort and capital.

7) Risk‑Based Prioritisation and QRM

Not every asset deserves an elaborate PdM model. GxP‑aligned programmes start with a criticality analysis: which equipment, instruments and utilities pose the highest risk to product quality, patient safety, data integrity or business continuity? Tools such as Quality Risk Management (QRM) and PFMEA help identify where predictive techniques will actually reduce risk rather than simply generating more data.

For high‑criticality assets, PdM indicators may be tied directly into deviation triggers, alarm priorities and escalation paths. For lower‑criticality assets, PdM may simply inform planning (for example, bundling a non‑critical pump seal change into an upcoming shutdown). The key is that risk assessments explicitly describe how predictive indicators are used, what their limitations are, and how they are considered in maintenance and CAPA decisions.

8) Implementation Steps in Regulated Plants

Implementing PdM under GxP is usually iterative. First, stabilise the basics: ensure that the asset register, PM plans, calibration programme and equipment qualification status are accurate and current. Second, perform a criticality and data‑readiness assessment to select a small number of pilot assets where failures are painful and good data is available. Third, define PdM use cases, choose indicators and analytics approaches, and design how PdM outputs will create or update work orders in the CMMS.

After the technical design, align PdM with the VMP and CSV expectations: requirements, risk assessments, test protocols and acceptance criteria. Train maintenance, engineering, QA and operations staff on what PdM indicators mean and how to act on them. Start with advisory mode (PdM makes recommendations while traditional PM remains in force), then gradually transition to allowing PdM to influence intervals where data and risk assessment justify it.

9) Data Integrity, Records and Investigations

PdM engines generate a new class of GxP‑relevant records: condition indicators, health scores, model outputs and recommendations. When these influence decisions on critical assets, they must comply with data‑integrity expectations: attributable, legible, contemporaneous, original and accurate (ALCOA+). That means audit trails for configuration changes, clear traceability from PdM output to work orders, and preserved histories that can be reviewed during deviations, investigations and inspections.

When a failure or near‑miss occurs, investigators should consider PdM signals alongside traditional maintenance records: Did indicators show an emerging problem? Were alarms acknowledged but not acted on? Did a model under‑ or over‑predict risk? Lessons learned from these investigations should feed back into model tuning, thresholds, SOPs and training, closing the loop between PdM insights and QMS processes such as deviations and CAPA.

10) Roles, Responsibilities and Governance

PdM adds responsibilities across maintenance, engineering, operations, QA and IT/OT. Maintenance teams interpret alerts, plan interventions and confirm findings in the CMMS. Reliability and data engineers design indicators, maintain models and evaluate performance. Operations provides local context and early warning on abnormal behaviour. QA oversees the GxP boundary: which PdM elements are in scope for validation, how records are retained, and how PdM is considered in investigations and audits.

Governance should be explicit: who can change PdM rules or model parameters; how new indicators are proposed, reviewed and approved; how conflicts are resolved when PdM suggestions conflict with fixed PM intervals or vendor recommendations. A clear RACI matrix and supporting SOPs prevent PdM from becoming a “shadow system” owned solely by a data team, disconnected from the plant’s formal QMS and maintenance governance.

11) Multi‑Site Standardisation and Scaling

Once PdM pilots prove their value, organisations usually face a scaling question: replicate locally developed logic or centralise it into a common toolkit and methodology. Multi‑site programmes often establish shared standards for data structures, condition indicators, criticality assessments, model validation and reporting, aligned with corporate QMS and engineering standards.

At the same time, PdM must respect site‑specific differences in equipment, duty cycles, regulatory commitments and organisational maturity. A practical approach is to define global building blocks—preferred sensors, analytics patterns, dashboards, documentation templates—while allowing sites to tailor which blocks they deploy and how they prioritise assets. Periodic cross‑site reviews and benchmarking of PdM outcomes (downtime reductions, maintenance cost profiles, audit feedback) help move the entire network up the maturity curve.

12) PdM for Utilities, Environment and Infrastructure

Some of the highest‑impact PdM use cases in GxP plants are not production skids but utilities and infrastructure: HVAC units that support classified rooms, chilled‑water and steam networks, WFI and clean‑steam systems, compressed air, nitrogen and power distribution. Failures in these areas can force line shutdowns, product holds or large‑scale impact assessments.

Condition‑based monitoring of these systems—combined with environmental monitoring (EM) and utilities qualification data—can highlight drift before it becomes a deviation. For example, increasing vibration on an air‑handling unit fan that supports Grade B space, or trending pressure‑drop behaviour in sterile gas filtration, can trigger maintenance earlier than a simple calendar interval would. PdM insights should feed into utilities validation reviews and risk assessments, demonstrating that the plant is actively managing utility reliability rather than treating it as invisible background infrastructure.

13) Using PdM Evidence in CPV, APR/PQR and Audits

PdM generates exactly the kind of trend evidence that regulators expect to see in Continued Process Verification (CPV), Annual Product Reviews (APR) or Product Quality Reviews (PQR). Reliability metrics, mean time between failure (MTBF), distribution of maintenance types (predictive vs preventive vs corrective) and correlations between equipment health and process performance help demonstrate that the process remains in a state of control.

In audits, being able to show how PdM findings led to proactive interventions, updated PM plans or design changes is powerful evidence of an effective QRM culture. The key is to integrate PdM metrics into established reporting structures rather than creating separate “PdM reports” that nobody outside the reliability team sees. When QA, engineering and operations all review the same dashboards and trend packs, PdM becomes part of how the site demonstrates control—not a side project.

14) Common Pitfalls and How to Avoid Them

Typical PdM failures in regulated environments are rarely about algorithms; they are about basics. Poor sensor placement, bad time synchronisation, missing asset context, unmanaged model drift and lack of clear action rules can all undermine trust. Another common issue is over‑promising—rolling out complex analytics on marginal data, then quietly reverting to calendar‑based PM when the models prove unreliable.

Avoiding these pitfalls means starting with strong instrumentation and data foundations, scoping realistic use cases, validating models against real‑world events, and keeping humans firmly in the loop. From a compliance standpoint, it is safer to under‑claim PdM’s influence and treat it as decision support until robust evidence accumulates, rather than rapidly replacing established PM intervals without a clear, documented risk‑based rationale and QA alignment.

15) FAQ

Q1. Can PdM replace time‑based preventive maintenance intervals?
Sometimes, but only with strong evidence and risk‑based justification. In many GxP plants, PdM initially operates alongside fixed PM intervals; over time, intervals may be optimised where data show that risk is controlled and regulators are comfortable with the rationale.

Q2. Is PdM itself a GxP system that must be validated?
If PdM outputs influence decisions on GxP‑critical assets—maintenance timing, calibration, equipment availability—it falls within the scope of CSV and the VMP. The level of rigour depends on criticality and complexity, but configuration, models and integrations typically require documented requirements, testing and change control.

Q3. Do we need advanced AI to benefit from PdM?
No. Many high‑value PdM wins come from basic condition monitoring, simple trend rules and better use of historian and CMMS data. Advanced analytics and AI can add value later, once data quality, governance and processes for acting on insights are mature.

Q4. How does PdM interact with TPM and OEE programmes?
PdM provides more precise signals about where problems are developing; TPM provides the culture, routines and ownership to act on those signals; OEE provides the metric framework to see whether reliability is improving. The three are complementary when managed under a common reliability and operations strategy.

Q5. Which functions should own PdM in a regulated plant?
Ownership is typically shared. Maintenance and reliability engineering lead the technical and analytical aspects; operations owns day‑to‑day response; QA governs the GxP boundary and data‑integrity expectations; IT/OT ensures secure, reliable infrastructure. Clear governance and a common roadmap keep PdM aligned with both business and compliance goals.

Related Reading
• Maintenance & Assets: Total Productive Maintenance (TPM) | OEE | Asset Calibration Status | Equipment Qualification (IQ/OQ/PQ) | Utilities Qualification (UQ)
• Quality & Risk: QRM | Deviation / Non‑conformance | CAPA | Data Integrity | Process Validation | CPV
• Systems & Data: MES | Manufacturing Data Historian | Environmental Monitoring (EM) | CSV | VMP