What is predictive maintenance?

Predictive maintenance’s main goal is to predict equipment failures based on certain parameters and factors. Once predicted, manufacturers take needed steps to prevent this failure with corrective or scheduled maintenance.

Predictive maintenance cannot exist without condition monitoring. Machines conduct continuous monitoring in real working conditions to ensure asset optimization. As with any maintenance strategy, predictive maintenance aims to:

• Reduce breakdown occurrence and maximize asset uptime by improving asset reliability
• Optimize operational costs by lowering maintenance work
• Improve maintenance budgets by reducing maintenance costs and maximizing production time

Predictive maintenance technologies

There is no singular technology that encompasses all of predictive maintenance. However, there are numerous condition-monitoring devices and techniques that manufacturers use to effectively predict failures and raise red flags when maintenance is needed.

Infrared thermography

Infrared thermography is a non-intrusive testing technology that is widely used in predictive maintenance. With infrared cameras, maintenance personnel can spot above-normal temperatures in equipment. Components that are worn out or have malfunctioning circuits tend to heat up--this displays as a heat spot on a thermal image. Infrared inspections can find these hotspots early on and repair equipment, reducing the chances of larger issues. Infrared is a versatile technology that can be used in a wide variety of machinery and infrastructural projects.

Acoustic monitoring

With acoustic monitoring, maintenance personnel can detect the sounds of gas emissions, liquid, or vacuum leaks in equipment at the sonic and ultrasonic levels. Ultrasonic technology has far more applications than sonic and can be more expensive; however, it is a much more reliable technology for machinery. Of course, these technologies supplement technicians' best tool: their ears. Sonic and ultrasonic technologies can supplement regular listening to better detect why a gearbox sounds wrong or where a possible leak might be.

Vibration analysis

Vibration analysis is used for high-speed rotating equipment. A technician uses handheld devices or real-time sensors in the equipment to monitor equipment functioning. When a machine performs at its peak, it emits a specific vibrational rhythm. When components begin to wear down, the vibration changes and a new pattern emerges. With constant monitoring, a trained technician can match vibration pattern readings against known failure possibilities and resolve a problem earlier.

Vibration analysis can detect misalignment, out-of-shape shafts, unbalanced elements, loose mechanical components, and motor issues. Technicians need to be well trained for the job as predicting vibration analysis is complicated. The main hindrance to vibration analysis is its prohibitive cost.

Oil analysis

Oil analysis is an effective tool in predictive maintenance. By checking oil conditions, technicians can establish the presence of contaminants. Oil analysis determines viscosity, water, and particle counts—and establishes the acid or base number. The main benefit of oil analysis is that its initial test results serve as a baseline for any new machinery and maintenance.

Other predictive maintenance technologies

Predictive maintenance employs several other techniques such as motor condition analysis, eddy current analysis, and more. Motor condition analysis outlines the functioning conditions of motors. Eddy current analysis spots changes in tube wall thickness. Other technologies that aid predictive maintenance are borescope inspections, computerized maintenance management systems, data integration, and condition monitoring. Choosing the right one for your organization is critical to success.

How does predictive maintenance work?

Here are the steps to start a predictive maintenance program:

1. Analyze the history of your equipment and the need for a predictive maintenance program.
2. Review all records about downtime, equipment faults, production and energy losses, regulation fines, and workplace safety levels.
3. Generate awareness for major stakeholders about the need for predictive maintenance, and get buy-in for the operations and maintenance teams.
4. Evaluate equipment inventory and appraise equipment condition.
5. Select an analytics platform able to integrate with your data source, analyze data, create equipment models, apply real-time data science on machine sensor data, and notify critical conditions and execute data-driven actions to avoid fatal downtime.
6. Choose the equipment to include in the program’s initial implementation and identify the KPIs for your program.
7. Collect the relevant data from various sources, including equipment sensors, maintenance logs, and historical maintenance records. Ensure that the data is clean, accurate, and accessible for analysis.
8. Cleanse and preprocess the collected data to remove noise, handle missing values, and standardize formats. This step is crucial for ensuring the quality and reliability of the data used for predictive analytics.
9. Identify data that can serve as input variables for predictive models and choose appropriate machine learning algorithms such as regression, classification, or time series forecasting models. Consider factors such as the type of equipment and the nature of failure modes.
10. Train the selected predictive models using historical data and validate their performance for the predictive maintenance program.
11. Deploy the trained models into your selected analytics production platform and connect it with existing data sources—ensure seamless integration to enable real-time monitoring and decision-making.
12. Define personnel roles at all stages and evaluate resource needs.
13. Roll out the program. Monitor the performance of the predictive maintenance program over time and continuously refine the models based on new data and insights. Implement feedback loops to incorporate lessons learned and improve the accuracy and effectiveness of the models.
14. Evaluate the impact of the predictive maintenance program on key performance metrics and adjust strategies accordingly to maximize the program's effectiveness and ROI.

The main elements of a predictive maintenance program are condition-based diagnostics that use predictive formulas and the Internet of Things:

Condition-monitoring sensors

The sensors that monitor machinery and provide real-time data are an essential part of predictive maintenance. Technicians can evaluate sensor data to ascertain a machine’s efficiency and its wear and tear in real-time. These sensors do what humans have been unable to do—track maintenance needs from inside the equipment without disrupting operations. Many parameters define sensor function, and they vary based on the machine. Parameters usually make use of vibration analysis, noise and temperature analysis, pressure and oil level analysis, and even electrical currents and corrosion to define functions.

The Internet of Things (IoT)

These sensors gather lots of data, and combined with the Internet of Things, this data can be collected and shared. Predictive maintenance depends largely on various sensors that connect assets to a centralized information storage system. The hubs function with wireless local area network connectivity or utilize cloud technology.

From this centralized space, assets can communicate, work in tandem, analyze data, and recommend any course of action. This ability to exchange information is what makes predictive maintenance efficient.

Predictive formulas

With predictive formulae, predictive maintenance takes a step further and becomes more than condition-based care. Predictive algorithms analyze collected data and identify trends that report when an asset will require repairs, servicing, or replacement. These algorithms are based on predetermined rules that constantly compare an asset’s current behavior with its expected behavior. Any deviation is an indicator of possible deterioration. Technicians can intervene at this point and prevent massive breakdowns.

Benefits of predictive maintenance

There are several benefits of predictive maintenance:

Reduced maintenance costs

Predictive maintenance can lower the costs of maintenance operations. This is especially important when organizations have to invest in the costs of labor, maintenance, replacement parts, tools, and equipment needed in the case of major failures.

Fewer machine failures

There is a lot of research on reducing machine failures. Regular machine and system monitoring can lower the chances of unexpected, large-scale failures. After two years of implementing a predictive maintenance program, the frequency and nature of machine failures often decrease.

Reduced downtime

With predictive maintenance, repairing equipment takes less time. Regularly monitoring and analyzing machine conditions helps maintenance personnel locate faulty components on all machines and resolve issues quickly. This reduces downtime and in many cases, prevents it completely.

Reduction in stocking

Often companies have to deal with large stock investments of various parts, which can lock up capital. If the parts are not used soon enough, their quality deteriorates and may go to waste. Instead of maintaining a large stock of parts in anticipation, ordering parts only when they are needed can reduce the costs of the stocking.

Increased lifespan of machinery

Detecting machinery issues (before they turn into catastrophic failures) can increase the lifespan of machinery. Having a condition-based predictive maintenance program in place ensures that equipment never reaches the stage of severe damage. The longer life of the equipment ensures a better return on investment for the organization.

Mean time between failure estimations

An additional benefit of predictive maintenance is being able to estimate the mean time between failures (MTBF). This refers to the most cost-effective time frame in which to replace machinery. Some companies tend to use a piece of equipment with all its faults and multiple repairs, with the misplaced notion that new equipment is an expensive investment. Being able to replace machinery at the end of its life, prevents high maintenance costs for worn-out machinery.

Increased production

Condition-based predictive maintenance programs need to be backed up by robust process systems, which increases the program’s efficiency. A comprehensive predictive program that includes parameter monitoring can improve operational efficiency and in turn enhances production numbers.

Heightened operator safety

With predictive maintenance, early warning signals can be put in place to prevent injuries from faulty machinery. Many insurance providers recognize and offer benefits to manufacturers who use a condition-based predictive maintenance program. Implementing this program can reduce insurance costs without compromising on coverage.

Verification of repairs

While fixing one issue, a repair may compromise other parts of a machine. Using vibration analysis, a maintenance team can detect any abnormal behavior after a repair. With predictive maintenance, companies can analyze data to plan and organize scheduled maintenance shutdowns, making the most of machine downtime.

Profits increase

Predictive maintenance management improves manufacturing operations and processing plants. A condition-based management system is worth more than the cost of the program. With predictive maintenance techniques, companies can lower yearly operational costs and reduce risks.

Challenges of predictive maintenance

A predictive maintenance program improves the longevity of equipment and reduces (or completely prevents) downtime that may cause errors or delays in the business network. Once properly implemented, predictive maintenance systems help machines anticipate a wide range of possible failures.

In the early stages of implementation, it may be difficult to connect to existing machinery and Enterprise Resource Planning (ERP) systems. However, with rapid technological developments, most of these challenges are no longer an issue. Seamless communication between machinery, sensors, attached devices, and employees is possible, making the system more efficient. Visual interfaces have narrowed the distance between man and machine. These interfaces take the shape of data visualization dashboards with data science capabilities able to process data in real-time and trigger alerts.

With new technology, maintenance managers can ensure that machine sensors gather reliable data in real time. Quality data makes all the difference to the efficacy of a predictive maintenance program.

The future of predictive maintenance

There may be a few hurdles to predictive maintenance program implementation; however, it remains an integral part of maintenance. All manufacturers who successfully tackle integration issues and automate processes can earn a major monetary and competitive advantage. All manufacturers that wish to remain relevant and functional in the future must adopt predictive maintenance practices.