Using PLCs for Predictive Maintenance: How to Monitor and Analyze Data for Improved Efficiency and Downtime Reduction

Predictive maintenance is a proactive strategy that employs data analysis methods and instruments to spot abnormalities in the operation and prospective flaws in machinery and procedures so you can address them prior to failure. Utilizing different software notifies individuals about maintenance which will be held in the future. In industrial settings, the primary focus is given to profit and earning and then to the safety of machinery and staff. The equipment downtime is often can be costly, which results in lost production. This decreased efficiency, and increased maintenance expenses. Old-style maintenance strategies, such as reactive or preventative maintenance sometimes are not sufficient and may not prevent unexpected downtime.

A PLC can serve the purpose of predictive maintenance for predicting defects and trouble before they arise and evaluating data patterns for abnormalities and oddities. It monitors and analyze the data in real-time which not only help improve efficiency but also reduce the downtime. So, Programmable logic controllers are widely used in industrial automation, monitoring, and control systems.

The benefits of implementation of a PdM system with PLCs, industries can also extend machinery life, increase the efficiency of production, and reduce maintenance costs. It also identifies the equipment issues before they become serious for the factory.

In this article, we’ll discuss predictive maintenance, its process and techniques, and how PLCs are integrated for predictive maintenance. We will also try to understand data acquisition and monitoring and analyzing by PLCs. And at the end, we will overview the Challenges of PLC-based Predictive Maintenance.

Predictive Maintenance: An Overview

What Is PdM?

Predictive maintenance shortly said PdM is a scheme that finds abnormalities in operation and potential flaws in machinery and procedures so you can address them before they stop of the process. In an ideal scenario, it permits the rate of maintenance to stay as minimal as practical to minimize unexpected reactive maintenance and save the costs involved with doing large quantities of preventive maintenance.

Process of Predictive Maintenance

Using historical and real-time data from multiple areas of your industry, predictive maintenance foresees issues before they arise. Predictive maintenance takes into consideration two key components.

• Monitoring the performance and condition of assets in real-time.
• Analysis of data related to maintenance work orders, such as information on equipment, repair history, and maintenance activities, to identify patterns.

Predictive maintenance involves a number of important components, with software and technology being one of them. In particular, artificial intelligence, the Internet of Things (IoT), and integrated systems allow for the connection, collaboration, and sharing of various assets and systems as well as the analysis and use of data.

These instruments gather data via software for, industrial controls, business systems, and sensors for predictive maintenance. They interpret it after that and utilise it to pinpoint any problems. Vibration analysis, thermal imaging, oil analysis, and equipment monitoring are some examples of predictive maintenance applications.

Predictive Maintenance Techniques

Predictive maintenance includes utilizing data which can be both real-time and historical. On the basis of data, it forecast when it is needed to conduct maintenance, instead of depending on predetermined schedules or reactive maintenance. This is a very helpful step to boost productivity and efficiency, decrease downtime, and avoid significant equipment breakdowns. Most condition-monitoring devices and approaches need sensors to gather data and communicate it with the available software. There are several techniques of predictive maintenance.

Using Sound Waves to Monitor Equipment Performance

In this technology monitoring machines’ sound waves are employed to detect. It detected flaws in their technical performance and identify the root of the problem. It is particularly beneficial for pipelines transporting liquids or gas. Although it is deemed less costly than other technologies, it has limited utility.

Using IR Thermography Analysis

In this technology employs Infrared lenses to detect excessive heat in pieces of equipment. It may be used to assess anything from small sections of equipment to plant systems and even whole facilities. It is regarded as one of the most flexible predictive maintenance methods available.

Predictive Maintenance by Oil Analysis

Oil analysis utilizes oil samples to measure equipment wear. It examines the particularities of oil samples, such as quantity and size, to identify machine damage. Setting up standards using past data is straightforward, and the first tests automatically define a baseline for the new system.

A hydraulic system, for instance, is made of two primary pieces – the spinning portions and the lubricating part. Once machines start deteriorating their operational conditions, the essential oil will include byproducts erosion and overheating. These particles show several possible faults and help organizations to rise with maintenance options before unexpected downtime starts.

Every firm working in the oil and gas sector may easily establish the criteria using oil analysis using historical data. With the new machine, the first tests immediately establish a baseline. The findings are saved to the database. If done correctly, oil analysis produces a wealth of information that helps a predictive maintenance management strategy succeed.

Vibration Analysis

With this method, the vibration of a machine is tracked using portable analyzers or real-time sensors. It is mostly used for speedily rotating equipment and is capable of spotting alignment issues, bent shafts, imbalanced parts, defective mechanical parts, and motor issues.

Motor Circuit Analysis

Electronic signature analysis is used in motor circuit examination to identify problems and possible equipment breakdown via electric motor parts. It may identify faults with the bearing, stator winding, coupling, rotor, anomalies in connected load, efficiency, and system load, among other things. Tests may be performed on motor circuits analyzers while they’re still operating and in approximately 2 minutes total.

PLCs and Predictive Maintenance

What Are PLCs and How Do They Work?

PLC is a sort of computer technology which is used for controlling many procedures and devices in the industry. It is also called the brain of machinery. It has the key to troubleshooting in situations of any malfunction. Programming of PLC is done for particular purposes, therefore analyzing the stages in the programme might lead to problem diagnostics and repair. The PLC accepts inputs from different instruments, switches, codes, sensors, and other data provided by the user, and produces its output signal for the action of motors, solenoids, indicators, alarms, etc., in a predetermined sequence. Each modification in the operation is performed by a PLC. Likewise, any fault may be found by studying the PLC activities. This is now the usual reactive maintenance whenever a malfunction arises. Modern technologies move further; in combination with IIoT equipment and data analytics, a PLC may serve as predictive maintenance by predicting defects and trouble before they arise, studying data patterns for abnormalities and oddities.

PLCs Used for Data Acquisition Work

During predictive maintenance, a PLC has to utilise data, which is obtained in process of the production process – data of measurements like vibrations, heat, electrical charge, voltage, sound frequencies, pressures, etc. These are obtained by different sensors.  Although this input is sufficient enough for ordinary troubleshooting, it’s not adequate for applications like preventative maintenance. The PLCs generally are designed to measure variables to signal yes/no, but not to detect deviations within specified ranges. This is due to the fundamental job of a PLC is really to manage the machine’s actions and just not data collecting which is accidental. If any modification needs to be done in PLC functioning, it takes a qualified programmer.  There are many alternative techniques of data collection however they could not be perfect like PLCs.

 For predictive maintenance, there are developments in technology, notably Edge Computing devices and Internet – of – things devices. These are now equipped with the latest PLCs. They make it easier to gather data from all machinery and they have the ability to analyze it further in order to provide data sets for cloud services. They also align the data further with the needs of the IT systems, providing transparency in addition to greater management and improved security via IT and OT cooperation. Newer PLCs also integrate with cloud platforms like “Microsoft Azure / Amazon Web Services, which facilitate data gathering and storage and give tools for customizing predictive maintenance procedures as needed.

Monitoring and Analyzing Data with PLCs

To discuss the monitoring and analyzing data with PLCs, it is necessary to overview what type of data PLCs can collect. With the use of sensors and other instruments used in industries PLSs can collect various types of data, which vibration data, pressure data, volume data, temperature data, and current data. Vibration data is used for unbalanced or misaligned parts. Temperature data can be used to detect overheating or underheating of equipment. Volume data tell about the certain value of materials. Pressure data can be used for pressure-related issues like leakage. and Current data detect leaks or other used to detect problems in power consumption.

Downtime Reduction

By integrating a contemporary PLC that can collect data from the factory floor and transfer it to IT-based enterprise and Cloud storage, connecting the factory floor to the commercial enterprise, unplanned downtime in production may be prevented. By gathering and analyzing machinery data over time, predictive maintenance models may be developed that can be used to schedule maintenance tasks in advance of unanticipated events. PLCs may connect directly to the cloud, and some have been approved for use with Microsoft’s Azure Cloud Platform, making it easier to swiftly and securely collect information from the plant floor for preventative maintenance.

Importance of PLCs for Predictive Maintenance

Factories exist to create items, but their main function is to earn a profit, and this can only be accomplished if the factory performs at peak efficiency and with no downtime. Each interruption in the production process leads to losses, therefore process oversight and control are critical, bringing the ball down to the PLC table. As previously said, the PLC works as the brain of the devices and machinery. It is designed to continuously regulate the operational parameters of the manufacturing facility. In the lack of sufficient control, any divergence from the system is likely to result in a stop or, worse, harm. Considering that most industrial processes operate in harsh and dangerous settings, particularly in the hydrocarbon industries, even little mistakes in monitoring may create serious safety issues, resulting in substantial destruction of machinery or even death. So, a PLC’s involvement in industrial process control becomes crucial to operate a plant safely through the use predictive maintenance plan in the factory.

Challenges of PLC-Based Predictive Maintenance

Execution of PLCs

Moving from outdated to contemporary technology may be a difficult process that requires significant financial expenditure. It is also a significant difficulty since changing from one technology to another may be a time-consuming procedure.

Data from databases are gathered, and features like alerts and warnings, and using algorithms to spot abnormalities are included. Hence, it represents one of the principal difficulties in predictive maintenance.

Technical Expertise

The maintenance personnel must be knowledgeable, competent, and well-trained in order to perform predictive maintenance effectively. Yet, it takes a lot of effort to train staff members and develop their skills. They should also be familiar with the operation of the programmer of PLCs. As a result, getting everything in order might take time.

 Safety Concern

Organizations always have a great issue with data management safety. Making ensuring that asset data and information are unlikely to be accessed by other parties is essential in terms of predictive maintenance.

Also, management must make sure that other parties should not have a lot of influence over the predictive maintenance programmer. Most crucially, data is never disclosed since doing so may harm the company.

Financial Hurdles

Implementing a predictive maintenance system using PLCs can be costly. This is because sensors need to be installed on a large number of machines. The extraction of information, and maintenance activities, also generate costs for companies.

Limitations of Forecast

 The development of a PdM model depends on the availability of pertinent data gained from PLCs, this is not sometimes correct and complete. The quality of the available information sources may not satisfy the requirements. This might lead to faulty projections, failure to recognize the need for maintenance, or provide false alarms.

Limitations of Machine Repair Work

When the number of attributes increases exponentially, the number of combinations increases as well and may become impossible to analyses. Missing failure events in the available data create a challenge for developing predictive maintenance models, which may result in inaccurate alarms in the resulting model since circumstances during a failure status may be ambiguous.

Conclusion

Predictive maintenance is a systematic strategy which finds problems in operation and potential flaws in machinery so that they can be addressed earlier to break down. Using PLCs for predictive maintenance can provide significant benefits for industrial processes, including improved efficiency and reduced downtime. They offer real-time monitoring and analyzing of data, and control of equipment, this faster the response times towards failures. PLCs are also highly reliable and can be easily integrated with other control. They can also perform in hazardous environments as well. These capabilities of PLCs enable improved efficiency and downtime reduction.

However, there are some challenges and limitations such as execution of PLCs, technical expertise, safety concerns, financial hurdles, and limitations of forecast and machines must also be considered.

Generally, predictive maintenance using PLCs is a powerful and reliable instrument to Monitor and Analyze Data for Improved Efficiency and Downtime Reduction. With proper implementation and management, predictive maintenance using PLCs can lead to significant cost savings, increased equipment lifespan, and improved safety of human beings and machinery.

This entry was posted on July 19th, 2023 and is filed under Uncategorized. Both comments and pings are currently closed.