June, 2023 - Vollständig verwaltete industrielle Datenplattform

Predictive Maintenance is a process that involves performance and equipment condition monitoring during regular operations to lower the chances of a breakdown.

The primary goal of predictive maintenance is to predict equipment failures by analysing specific parameters and factors. Based on the prediction report, manufacturers/engineers need to take corrective steps to prevent loss.

Predictive maintenance uses data analysis tools and techniques to identify anomalies in industrial operations and possible failures in equipment and processes so you can fix them before they lead to an outage.

Ideally, predictive maintenance programs allow for the lowest possible maintenance frequency to avoid unplanned reactive maintenance without incurring the costs associated with too much preventive maintenance.
Before we dive deeper into predictive maintenance let’s first understand how is it different from preventive maintenance.

How does Predictive Maintenance differ from Preventive Maintenance?

In today’s fast-paced industrial landscape, equipment maintenance plays a critical role in ensuring smooth operations and minimizing downtime.

As a result, maintenance strategies have evolved over time to better address the needs of businesses and their assets. Among these strategies, predictive and preventive maintenance are two of the most widely recognized approaches.

But how do they differ, and which one is the right choice for your organization?

In the table below, we will delve into the key differences between predictive and preventive maintenance, discuss their advantages and drawbacks, and help you make an informed decision when it comes to maintaining your assets effectively and efficiently.

So, let’s embark on this journey to better understand these popular maintenance methodologies and optimize your maintenance management practices.

Predictive Maintenance	Preventive Maintenance
Occurs as per real-time need based on machine operation data to identify issues at the nascent stage to avoid interruption in production.	Occurs on the same schedule every cycle - whether or not maintenance is needed.
Downtime might be necessary but it can be timed, so that it is least disruptive	Machine downtime is required
Identifies possible machine failure before it can occur	Includes activities such as equipment & component inspection, cleaning, repair, etc.
The demand for spare stock quantity is lower than Preventive Maintenance	The demand for spare stock quantity is higher than the Predictive Maintenance

Requirement for Predictive Maintenance - why it matters?

Predictive maintenance is a proactive approach to maintaining equipment and assets, using real-time data and advanced analytics to identify potential issues before they lead to failures.

This strategy is crucial for modern organizations as it minimizes downtime, increases asset lifespan, and reduces maintenance costs.

By anticipating and addressing problems before they escalate, businesses can improve operational efficiency, maintain safety standards, and enhance overall productivity.

In a highly competitive industrial landscape, the requirement for predictive maintenance is more important than ever, as it helps organizations stay ahead of the curve, optimize resources, and maintain a competitive edge.

It can’t exist without condition monitoring. To optimise assets, carry out continuous monitoring under real-time working conditions. Predictive maintenance aims to:

Improve asset reliability to reduce breakdown occurrence and maximising asset uptime
Lowering maintenance work to optimise operational costs
Improve maintenance budgets by lowering maintenance costs and maximising production time.

What are some common Predictive Maintenance Technologies?

There is no singular technology for predictive maintenance yet. Manufacturers/Engineers are dependent upon condition-monitoring devices and techniques to predict failures and raise red flags when needed.

Some of those techniques are:

Acoustic Monitoring

Maintenance staff can identify gas emission, liquid, or vacuum leaks in equipment at the sonic and ultrasonic level by utilising acoustic monitoring.

Although more expensive, ultrasonic technology is considered a more dependable machinery technology, and it has a broader range of applications than sonic technology.

It’s important to note that while these technologies can aid in detecting issues, technicians’ ears remain their most valuable tool.

Sonic and ultrasonic technologies can be utilised alongside regular listening to more accurately identify the root cause of unusual gearbox sounds or potential leak sources.

Infrared Thermography

Predictive maintenance often utilises infrared thermography, which is a non-invasive testing technology. By using infrared cameras, maintenance staff can detect higher-than-normal temperatures in machinery. Faulty components or circuits that are worn tend to generate heat, which can be visualised as a hotspot on a thermal image.

By conducting infrared inspections, these hotspots can be detected early on, and necessary repairs can be made, ultimately minimising the likelihood of more severe problems. Infrared technology is versatile and applicable in various machinery and infrastructure projects.

Oil Analysis

In predictive maintenance, oil analysis is a useful tool that enables technicians to identify contaminants by examining the condition of the oil. Oil analysis involves evaluating viscosity, water content, particle counts, and determining the acid or base number.

One significant advantage of oil analysis is that its initial test results can serve as a reference point for future machinery and maintenance, allowing for early detection of any concerning changes in the oil’s properties.

Vibration Analysis

To monitor the performance of high-speed rotating equipment, vibration analysis is utilised. A technician may use handheld devices or real-time sensors within the equipment to track its functioning.

During optimal operation, a machine emits a distinctive vibrational rhythm. However, as the components begin to wear out, the vibration pattern changes, and a new one emerges.

By constantly monitoring this vibration pattern, a trained technician can compare the readings against known failure possibilities, enabling them to detect and resolve problems early on.

Vibration analysis is capable of identifying a range of issues, including misalignment, out-of-shape shafts, unbalanced elements, loose mechanical components, and motor problems.

However, the technique requires highly skilled technicians due to its complexity. The primary challenge associated with vibration analysis is its prohibitive cost.

How does Predictive Maintenance work?

Predictive maintenance uses historical and real-time data from different areas of your operation to identify problems before they occur. There are three main areas in your organisation that play a role in predictive maintenance.

This includes real-time monitoring of asset health and performance, analysis of work order data, and benchmarking of MRO asset utilisation.

To implement a predictive maintenance program, the following steps are typically taken:

Assess the equipment’s history (at least 2 years of data analysis required) and based on that create a plan for predictive maintenance.
Review all relevant records, such as downtime, equipment malfunctions, production and energy losses, regulation fines, and workplace safety levels.
Raise awareness among key stakeholders about the need for predictive maintenance, and secure the support of the operations and maintenance teams.
Evaluate the equipment inventory and assess the equipment’s condition.
Choose the equipment to be included in the initial implementation of the program.
Create detailed records of each system and its components.
Evaluate any pre-existing preventive or predictive maintenance protocols.
Determine the frequency and schedule for the predictive maintenance program.
Define personnel roles at each stage and evaluate resource requirements.
Organise the program and integrate it with scheduling systems.
Establish a computerised maintenance management system (CMMS).

What are the benefits of Predictive Maintenance?

Let’s explore the advantages of predictive maintenance and why it has become an essential aspect for organisations today:

1. Reduced incidence of Machine Failures

Extensive research has been conducted to minimise machine failures, and it has been established that regular monitoring of machines and systems can significantly reduce the likelihood of unforeseen, large-scale failures. In many cases, after the implementation of a predictive maintenance program for two years, the frequency and severity of machine failures tend to decrease.

2. Reduced Downtime

The implementation of predictive maintenance leads to quicker equipment repair times. By consistently monitoring and analysing machine conditions, maintenance personnel can easily identify faulty components across all machines and efficiently resolve any issues. This significantly minimise downtime and, in some cases, even eliminates it altogether.

3. Reduced Maintenance Costs

The utilisation of predictive maintenance can effectively reduce maintenance operation expenses, which is particularly crucial when organisations have to bear the costs of labour, replacement parts, maintenance, tools, and equipment required for significant failures.

4. Decrease in Inventory Stocking

Many companies are faced with significant capital investments in various parts, which can tie up their resources. Furthermore, if these parts remain unused for an extended period, their quality may degrade, leading to waste.

Instead of maintaining a large stock of parts in anticipation of machine failures, ordering parts only when they are required can help reduce the expenses associated with stocking.

5. Extends Lifespan of Machines

By detecting machinery issues before they reach catastrophic levels, equipment lifespan can be extended significantly. Implementing a condition-based predictive maintenance program ensures that equipment is serviced before it deteriorates beyond repair.

The longer lifespan of the machinery provides a better return on investment for the organisation.

6. Estimating Mean Time between Failures

Predictive maintenance offers the added advantage of being able to estimate the mean time between failures (MTBF), which refers to the most cost-effective time to replace machinery.

Some organisations may continue to use equipment despite multiple repairs and faults, believing that purchasing new equipment is an expensive investment.

However, being able to replace machinery at the end of its life can prevent high maintenance costs associated with worn-out equipment.

7. Increase in Production

Robust process systems are necessary to support condition-based predictive maintenance programs and increase their efficiency.

A comprehensive predictive program that includes parameter monitoring can improve operational efficiency, which leads to increased production numbers.

8. Verification of Repairs

Vibration analysis can detect any unintended consequences of a repair that may compromise other parts of a machine. By implementing a predictive maintenance program, companies can analyse data to plan and schedule maintenance shutdowns and maximise the use of machine downtime.

This enables maintenance teams to verify repairs and identify any abnormal behaviour, ensuring that equipment operates at peak performance.

How can Paze Industries help?

Predictive maintenance is a critical aspect of any industrial operation, as it helps to minimise downtime and improve overall efficiency.

The first step in implementing a successful predictive maintenance plan is data gathering and analysis. Developing a data platform is a time-consuming and resource devouring process.

However, Paze Industries has developed a low-code platform that spares you from wasting valuable time, making it easy for you to plan and execute your predictive maintenance plan.

One of those success stories is “Emco” which you can access by clicking here.

The digitization of manufacturing processes results in the networking of the associated machines, production systems and tools. This also affects maintenance. While preventive maintenance still dominates in many areas today, as technologies become cheaper, so-called predictive maintenance concepts are increasingly spreading.

However, machine builders who want to offer their customers added value based on the new maintenance approaches must switch to data-based business models.

Our customers use the SF platform to collect and analyse data from networked machines and systems. The Industrial Internet of Things (IIoT) is currently turning numerous sectors and industrial sectors upside down.

In mechanical engineering, the networking of machines and systems generates data with great potential that industrial companies can use, for example, to optimise their machine development.

Networking allows local machine maintenance to be expanded to include centralised data analysis, from which, among other things, the expected failure time of a component, such as a seal or a bearing, can be derived.

By comparing the machine and system data recorded during operation with other data, such as idealised models, the software uncovers errors and faults as they arise.

Often long before the incident occurs. Such data-based “clairvoyance” not only lowers maintenance costs, it also reduces the machine failure rate.

Anticipate and delay maintenance events

Paze enables continuous 24/7 monitoring of machines and systems in real time. Intelligent sensors integrated into production machines collect the data generated during production and send it to our cloud-based IIoT platform.

This prepares them and enables trained users to draw conclusions about faults in the system from the recorded noises, speeds or temperatures. The models are often easier than you think, especially when it comes to maintenance.

In most cases, the temperature is actually enough as a decisive parameter. In the event of service, technicians can use the collected system data to work on troubleshooting in a targeted manner.

A machine model enriched with historical data can be used to anticipate maintenance events and even delay them to an optimal point in time by automatically changing process parameters. The consequences are reduced maintenance cycles and times.

Automatic alarms when the limit is exceeded

A wide variety of systems and machines, ranging from production systems, wind turbines or aircraft turbines to printing presses, motor vehicles or cranes, can be monitored and maintained worldwide using predictive maintenance via the Internet.

Communication usually starts in the networked systems, where sensors, measuring stations or probes record and transmit conditions such as temperature, vibrations, utilisation or wear.

For the evaluation, product and service experts set certain limit values that must not be undershot or exceeded. If this is the case, the system automatically triggers an alarm and sends a notification, often by email or SMS.

A crane manufacturer, for example, defines wind speed limit values. If the crane driver still tries to load a ship from a critical wind force above the limit value, this triggers an alarm that automatically reaches the responsible crane operator.

This also allows grenade claims to be better evaluated. There is also great potential for savings here.

Lifetime determination by acoustic patterns

An analysis method frequently used in predictive maintenance is what is known as acoustic pattern recognition. The service life of a specific part or component, such as a valve, can be determined on the basis of changes within an acoustic pattern.

Using artificial intelligence (AI) and machine learning, complex measured values are assigned meanings, on the basis of which data scientists can make assessments.

For example, the current state of wear of the drill can be read from the vibrations of a table in a CNC machine. Is it new, already worn or already worn out? More precise predictions are also possible, such as: “The drill has reached 15 percent of its lifetime.”

Efficient production is dependent on the functionality of your plants and technical systems. A technical availability of at least 95 percent of the possible operating time is considered ideal.

As part of predictive maintenance measures, an automatic detection of frequently occurring errors can be implemented in the machine.

For example, the identification of encoder errors in sensors or deviations in machine calibration. Thanks to the data-supported, continuous and always up-to-date insight into the system used, potential for improvement can be identified and implemented at an early stage, for example by comparing it with a digital model, and the availability of the machine can be increased.

Paze can be easily integrated into a wide variety of IT systems

In order to digitise the production processes and manage them appropriately, Paze helps users to have direct, uncomplicated access to the operating and status data of the system or machine.

In order for this to work, Paze can be seamlessly integrated into the IT systems of a wide variety of manufacturers. We provide a REST API that our customers can use to access machine data, query results and other functionalities such as versioning or automation.

The topic of scaling is an important point when choosing the right technology, because the more successful an IIoT application is, the larger the amount of data that has to be processed over time.

Platforms that are not set up to manage ever-increasing amounts of data will quickly reach their limits. With Paze, all services – from messaging to database to API services – can be scaled and run multiple times in parallel, so that resources can be increased (or reduced) seamlessly.

Without data, IIoT would not be possible. But not without trained employees who are familiar with the handling and analysis of data.

Specialists such as data analysts or data scientists are far too seldom to be found in medium-sized companies these days, or are often not available at all.

Without employees with qualified data expertise, however, it will be difficult for companies to create value based on their IIoT platform.

Low-code platform as a game changer

For this reason, Paze offers a platform based on low code. Instead of using classic text-based programming languages, our low-code platform supports the development of processes with visual user interfaces and other graphical modelling techniques.

This makes it possible for our customers, who have a great deal of machine expertise but only limited IT resources, to configure their own applications and apps and independently evaluate the status data of their machines and systems without professional programming knowledge.
Predictive maintenance based on low code is a real game changer for our mechanical engineering customers

Frequently Asked Questions on Predictive Maintenance (FAQs)

1. Why is Predictive Maintenance so important?

When predictive maintenance works effectively as a maintenance strategy, machines are only serviced when it is needed. This means that condition-based maintenance is carried out just before a failure is to be expected. This brings several cost savings, as maintenance time is effectively minimised. This also reduces production downtime and reduces the cost of spare parts and accessories.

2. For which applications is Predictive Maintenance suitable?

Applications that lend themselves to predictive maintenance include those that have a critical operational function and also have failure modes that can be inexpensively predicted through regular monitoring.

3. How is Predictive Maintenance performed?

The aim of predictive maintenance is to determine the best time to carry out work on an asset so that maintenance frequency is as low as possible and reliability is as high as possible without incurring unnecessary costs.

Data analytics for predictive maintenance and leveraging the Internet of Things are key to implementing a successful predictive maintenance program, as is the use of sensors and predictive maintenance techniques.

4. What tools are used for Predictive Maintenance?

Constant condition monitoring helps identify problems as it can reveal the exact reason why equipment is failing, thereby increasing equipment reliability. In addition, condition monitoring can help predict future anomalies and improve asset reliability.

Also the maintenance logs of the individual plants can be filtered and analysed if you have a large amount of data. However, you need many assets of the same or similar type to accurately capture the necessary predictive maintenance process flow.

Maintenance logs also help identify which facility needs more maintenance than required. In addition, it is possible to determine what type of maintenance activities occur, e.g. the replacement of parts, etc.

In addition, the evaluation of based reports is one of the most important tools of predictive maintenance. These reports contain important information and details that are helpful in understanding the plant and predicting plant performance, failure, etc.

The system reading is also applicable to some types of systems that break down periodically or start to overheat after a certain period of time. Many systems only come for maintenance for this reason.

Sometimes it’s hard to keep track of how long a device has been working. With this preventive maintenance software tool, you will be alerted and able to stop the machine and avoid a breakdown.

5. Which services are used for Predictive Maintenance?

The services enabled by predictive maintenance can be divided into four main groups. On-premises and Infrastructure as a Service can be self-managed and Platform as a Service and Software as a Service are managed by the responsible IT provider.

Some platforms like Paze have application templates like OEE for monitoring alarms, congestion or a service control room. This gives you an extra jump start as you can go straight to the customer and start iteration cycles. You can develop value creation very early in the project.

6. How does a Predictive Maintenance program starts?

A predictive maintenance program (PdM) anticipates the future condition of property, plant and equipment and makes timely and informed maintenance decisions. PdM – like the idea of Industry 4.0 – depends on the convergence of information technologies (IT) and operational technologies (OT). The key to a successful predictive maintenance program is to bring people, processes and technology together and to clearly define the desired goals.

SMEs in particular often do not have the resources to develop a completely new solution. Paze offers an end-to-end solution from the edge to the app. Where required, software modules are integrated into the existing IT infrastructure. So there is no rude awakening later when scaling and rolling out.

Predictive Maintenance Steps:

Once the primary purpose and focus areas for a move to predictive maintenance have been identified, assessment of the status quo should begin.

The first task is to document the processes and systems currently in use to identify what is working well and where there are gaps in knowledge and skills. This includes looking at the infrastructure and identifying critical workspaces and data collection points.

The assessment considers and assesses an organisation’s readiness for digital transformation, including a gap analysis that not only documents the status quo of processes and technology, but specifically describes how close or far that status quo is to digital readiness.

This assessment will result in a pilot project, a production test bed that will employ the technologies and processes necessary to demonstrably fill some of the gaps identified in the assessment. Paze’s rapid prototyping function also helps to quickly create a high-quality prototype that has access to all important data.

7. How to improve the performance of Predictive Models?

It’s not just data, it’s people too that make this work. There are likely to be experts in the company who know a machine or process inside out and have been working with it for many years.

The digital data now being collected and used is paramount, but the insights the experts are gaining – about things for which there is no data or has never been measured – can contain invaluable information that helps validate the results. These people are a valuable asset and an important part of any digital strategy.

Examples of using Predictive Maintenance

1. Predictive maintenance in Manufacturing

One industry where predictive maintenance pays off is manufacturing, which regularly uses large and expensive tools and equipment. If such equipment fails, an entire enterprise that depends on manufacturing could incur large losses that may well outweigh the installation and operational costs of predictive maintenance technology.

2. Predictive Maintenance for Mechanical Engineering

For machine builders in particular, predictive maintenance is of great interest as a service for their customers. Assets and devices whose sensors are already integrated into predictive maintenance software can bring customers significant savings in maintenance and service provider costs.

Machine builders can also offer their products with additional predictive maintenance services, which increases the value of the products and can lead to long-term customer loyalty.

3. Predictive Maintenance in the Automotive Industry

In automotive production, planned or unplanned downtime and the associated costs can cause a significant setback. With predictive maintenance, it is possible to constantly monitor the condition of industrial equipment in real time and predict the likelihood of failures. This improves operational efficiency and reduces equipment maintenance costs.

4. Predictive Maintenance in the Oil and Gas Industry

In the oil and gas industry, there is often a lack of overview of the condition of the plants at remote offshore locations. Maintenance technicians would periodically visit these locations to check equipment condition and perform oil analysis, even when not necessary.

With predictive maintenance, oil and gas companies can assess the health and performance of their assets and schedule maintenance only when an abnormal problem is identified.

5. Predictive Maintenance with Machine Learning

Nowadays, machine learning and AI simplify and improve numerous processes and things. Plant maintenance is one of them. Today, you can leverage predictive maintenance along with machine learning statistics and algorithms to prevent large losses and anomalies.

6. Predictive Maintenance and Condition Monitoring

Condition monitoring is the monitoring of the health of a system to detect changes that would indicate damage or impending failure. It enables operators to identify and correct problems (through repair and maintenance procedures) before they lead to equipment failure.

Predictive maintenance refers to the planning of corrective maintenance actions based on predictions about how a system will evolve. These predictions are based on data obtained through condition monitoring and plant-specific knowledge.

In other words, predictive maintenance is one of the ways condition monitoring can be used. The two methods complement each other and relate to different types of use and evaluation of sensor data.

7. Predictive Maintenance and IoT

Today, most modern industrial machines already have a variety of different sensors that continuously collect data and make it possible to set up models for predictive maintenance. With these sensors collecting real-time data across different devices, systems, assets, and locations, IoT-based predictive maintenance enables manufacturers to effectively predict and plan for events such as equipment failures or replacement of spare parts.

8. Predictive Maintenance and the Intelligent Factory

A smart factory or machine park are digitised production facilities that use networked devices, machines and production systems to continuously collect and exchange data. This data is then used as a basis for making decisions to improve processes and resolve problems.

The intelligent manufacturing processes employed in a smart factory are enabled by a variety of technologies including artificial intelligence (AI), big data analytics, cloud computing and the Industrial Internet of Things (IoT) and this provides the optimal conditions for a successful implementation of predictive maintenance.

9. Predictive Maintenance and Industry 4.0

As the Internet of Things (IoT) continues to advance, companies are beginning to adopt an Industry 4.0 approach to the manufacturing sector. The further development of AI and ML will support predictive maintenance and ultimately give companies an extreme advantage over those who do not rely on Industry 4.0.

10. Predictive Maintenance and Edge Computing

Edge computing reduces the load on networks and other IT infrastructure and keeps costs down. The technology involves the use of devices that analyse the data collected directly on the factory floor, rather than sending it to a cloud. Edge computing can also help maximise the uptake of IIoT-based predictive and prescriptive maintenance.

For some IIoT implementations, internet connectivity is not always available due to factors such as remote locations or the unreliability of cellular connections.

Conclusion

Paze offers optimal solutions for SMEs in mechanical engineering and in the manufacturing industry. With our modular end-to-end solution, we have exactly the right building blocks that our customers need to enrich the existing IT systems (such as MES, ERP, ticket, etc.) with the necessary depth of data and evaluate them extensively.

This starts as early as the planning phase, where you can use our rapid prototyping tool to brainstorm use cases and get everyone on board.
The platform can be set up and operated within a few days – with the latest technology and the highest security standards. We have an excellent 1st, 2nd and 3rd support team including ticketing system and telephone support.

We offer both low-code tools for non-coding teams and access to the REST API for developers and system integrators. Our super-efficient start-up out-of-the-box solutions and our customer success team with industry backgrounds can help your users find the most value in their data.

Part 1: Assessment of Internal Factors

Factor 1 - Know your machines

Factor 2 - Failure Modes

Factor 3 - Machine’s Environment

Factor 4 - Matching Use Case to Data Source

Part 2: Assess the available technologies

1. Low-code development

2. Integration capabilities

3. Time-to-market speed

4. Compatibility with existing technologies

5. Scalability and flexibility

6. Vendor support and documentation

Future Proof Condition Monitoring

1. Modular and extensible architecture

2. Data analytics capabilities

3. Open APIs and interoperability

4. Scalable data handling

5. Flexibility in data sources

6. Vendor ecosystem and partnerships

7. Future roadmap and innovation

Ready to revolutionise your operations?

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