What is maintenance in industry?
Plant maintenance in industry is the set of operations necessary to ensure that a factory’s facilities, processes and machinery are in optimal operating condition. This contribute to extending their service life while ensuring safety, energy efficiency, and operational continuity.
Designing a maintenance plan is absolutely essential for any company, but each organisation decides which resources are devoted to each of the existing types of maintenance (corrective, preventive, and predictive) according to its strategic priorities, budget constraints, and technological capabilities.
However, even when though we have been talking about the evolution towards Industry 4.0 for years, many companies still rely heavily on corrective maintenance, which is indicative of the lack of a firm commitment from executive leadership to the adoption of key systems for smart factories, such as predictive maintenance.
Nonetheless, an insufficient investment in preventive and, above all, in predictive maintenance is highly detrimental to a company’s operations. Corrective maintenance merely addresses issues after they occur, leading to unplanned downtime, increased repair costs, potencial safety hazards, and even regulatory non-compliance. Therefore, it does not prevent, for example, catastrophic failures, product loss or accidents.
In any case, before explaining what predictive maintenance is and its strategic relevance for the industrial refrigeration sector, we will pinpoint the main differences between the different kinds of plant maintenance in industry and we will explain why preventive maintenance remains a fundamental pillar for any business.
Why is preventive maintenance important?
Having highlighted the similarities and differences between predictive, preventive and corrective maintenance, it is crucial to emphasize that they are not mutually exclusive. An effective maintenance strategy balances all three approaches to optimize operational performance while mitigating risks.
That being said, the allocation of resources (material, financial and human) to each type of maintenance reveals the leadership’s vision regarding factory modernisation, regulatory compliance, and environmental sustainability.
Preventive maintenance, in this sense, is the bridge between reactive and proactive strategies. Specifically, it consists of scheduled and systematic inspections of the factory’s facilities to identify potencial isuues before they escalate into costly failures. This approach minimizes production disruptions by allowing maintenance activities to be planned during non-peak hours or scheduled downtimes. Apart from inspections, preventive maintenance includes actions such as oil changes, adjustment/replacement of components, calibration of equipment, and cleaning of critical systems.
Furthermore, by acting proactively, companies gain a better understanding of asset conditions, which facilitates strategic replacement planning based on estimated useful life. For this reason, it is crucial to keep a detailed and up-to-date digital record of inspections to achieve an optimal maintenance status. Leveraging computerized maintenance management systems (CMMS) can further enhance record-keeping and decision-making.
However, despite its advantages, preventive maintenance does not fully utilize the potential of industrial big data analytics. Companies aiming to transition into smart factories should progressively increase their investment in predictive maintenance, as it enables real-time condition monitoring and data-driven decision-making.
Differences between predictive, preventive, and corrective maintenance
Corrective Maintenance:
- Failure has already occurred.
- Does not prevent:
- Machine failure.
- Unexpected production halts.
- Potencial hazards to the workforce.
- Environmental impact due to unexpected leaks or spills.
- Does not leverage big data or predictive analytics.
- Requires minimal technological tools.
- The main responsible is the supervisor or maintenance team.
Preventive Maintenance:
- Failure has not yet occurred. Maintenance is performed based on a predefined schedule, by protocol.
- Does not prevent:
- Machinery failure related to external elements.
- From halting production unexpectedly.
- Potencial hazards to the workforce.
- Energy inefficiency caused by undetected early-stage issues.
- It does not use big data intensively.
- It does not need the most advanced technological tools, although it does require sensors.
- The main responsibles are the supervisor and the factory’s monitoring system.
- Increases / Improves:
- Equipment lifespan.
- Overalll reability and compliance with quality standards.
- The efficiency of production processes.
Predictive Maintenance:
- Failure has not yet occurred; maintenance is performed based on precise predictions, considering all relevant variables and intervention timelines.
- Prevents:
- Machinery failure caused by external or internal factors.
- Unexpected production halts.
- Potencial hazards to the workforce.
- Excessive energy consumption resulting from undetected real-time issues.
- It uses big data intensively; Relies heavily on big data, AI an IoT tecnologies.
- Requires advanced tools, including, machine learning algorithms, real time systems, as well as sensors, automatons, etc.
- The main responsibles are the supervisor and fully integrated automation systems.
- Enhences:
- Predictive planning and cost reduction.
- Asset performance and reliability.
- Energy efficiency and operational sustainaility.
Industrial refrigeration maintenance
When discussing predictive analysis in industry, we highlighted its ability to identify correlations between key variables to predict future system behaviors. Achieving this level of foresight requires real-time data collection, historical records, an AI-driven analytics.
Predictive industrial maintenace is one of the least common yet most valuable approaches for smart factories, as it delivers unique advantages, particularly in industrial refrigeration.
Industrial refrigeration companies cater to diverse sectors, such as food processing, pharmaceuticals, and logistics. Each sector faces distinct maintenance challenges, and inefficiencies in refrigeration systems can lead to severe consequences. For example:
- In logistics, a refrigeration failure can cause delays, product loss, and reputational damage.
- In pharmaceutical facilities, an ineffective monitoring of a lab’s refrigeration system can ruin a vaccine, a drug or the research’s breakthroughs.
The goal of predictive industrial refrigeration maintenance is not only to resolve immediate issues but also to anticipate and prevent long-term maintenance risks.
To achive this, large-scale data extraction from refrigeration equipment, stock levels, energy consumption, and environmental factors is essencial. This information is then analyzed using advanced monitoring techniques, enabling predictive insights for improved decision-making. IoT integration and AI-driven diagnostics further enhance systtem reliability and energy efficiency.
Industrial refrigeration maintenance techniques
Below are some of the most widely adopted predictive maintenance techniques in industrial refrigeration:
- Vibration analysis. It takes into account those appliances’ characteristics that provoke vibrations (such as rotation speed or type of support) to detect anomalies and diagnose them.
- Lubrication analysis: Evaluating lubricant quality helps detect metal particles or contaminants that may indicate premature wear of critical components.
- Electrical current monitoring: Increased energy consumption can signal malfunctions in motors, fans, or compressors.
- Digital Twin technology: Virtual models replicate equipment behavior in real-time, allowing for scenario simulations and highly accurate failure predictions.
- Vibration analysis: Takes into account factors such as rotational speed, support type, and gear mechanisms to detect and diagnose anomalies.
- Temperature monitoring: Just as products must remain within optimal temperature ranges, so too must system components to avoid irreversible damage.
- Thermographic cameras: Used to identify overheating in electrical or mechanical components.
- Ultrasound diagnostics: Detects leaks, electrical issues, and mechanical failures that are otherwise difficult to identify.
- AI-powered maintenance platforms: These combine machine learning and historical data analysis to improve failure predictions and recommend corrective actions autonomously.
By integrating preventive and predictive maintenance strategies, companies can optimize asset performance, reduce downtime, and enhance overall efficiency, ensuring their long-term competitiveness in an increasingly digitalized industrial landscape.
