How does predictive maintenance lead to more sustainable business management?

Knowing well in advance when a machine will fail prevents unplanned downtime and broken equipment. On average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and reduces maintenance costs by 25%*. It is based on advanced analytics for performing industrial-scale maintenance.
Predictive maintenance helps companies cut costs and gain more control over equipment and machinery. But how exactly does it work, what does it require, and does it actually provide a return on investment?
Industrial production has evolved tremendously over the last few centuries. From steam and water power, through assembly lines, to digitalization.
The concept of predictive maintenance originated during World War II. C.H. Waddington, a consultant to the British Air Force, proposed a new maintenance plan. Called "condition-based maintenance" (CBM) at the time, it improved the performance of fighter aircraft like never before.
CBM was further advanced by commercial industries beginning in the late 1940s. In the 1980s and 1990s, digital systems such as CMMS and EAM software made CBM more accessible, eventually leading to the predictive maintenance that we know today, supported by IoT technology.
Industry 4.0 - the so-called fourth industrial revolution - is currently in full swing. It is characterized by interconnectedness and a huge amount of available information. Productivity has been constantly increasing thanks to modern machinery. These machines are highly complex and require large investments.
In the context of Industry 4.0, maintenance means much more than simply preventing downtime of individual machines. Machines are increasingly interconnected within the entire production chain. Indeed, one failed machine can shut down the entire production process. Today, flawed maintenance strategies can reduce a plant's total productive capacity by 5 to 20%*.
Long and uninterrupted service life of capital-intensive, highly integrated machines provides a significant competitive advantage. This includes efficient and well-coordinated predictive maintenance.
Predictive maintenance is all about continuously analyzing systems and processes to perform maintenance as reliably, efficiently and cost-effectively as possible. Through systematic monitoring, companies can near-real-time Schedule maintenance with minimal disruption to production.
The idea is to analyze real-time data and perform maintenance only when truly necessary, rather than periodic maintenance that may be unnecessary. This is done through constant monitoring, data collection and analysis.
Maintenance is traditionally performed on a periodic maintenance basis. Periodic maintenance involves performing maintenance on a set schedule, regardless of whether it is needed or not. Predictive maintenance is based on continuous monitoring and intervening as soon as the data indicate that maintenance is actually needed. Both methods prevent unplanned production downtime, but predictive maintenance avoids unnecessary maintenance interventions and thus the waste of resources, also provides timely warning of potential outages, effectively avoiding downtime.
With periodic maintenance, you do not take into account the actual condition of the equipment; it is done on schedule, even if the machines are still working fine. This can lead to excessive maintenance, high costs due to premature replacement of parts and additional risks for employees. Predictive maintenance ensures that machines are used optimally by continuously monitoring their condition and intervening only when really necessary.
Compared to preventive maintenance, manufacturers using predictive maintenance report: 19% fewer unplanned downtime and 87% fewer defects**.
To implement predictive maintenance, each type of company must adapt the approach to its own situation. Depending on the industry, the variables and tools differ. However, there are six critical components needed to implement predictive maintenance in your business:
1. Data collection and preprocessing.
Real-time data are essential for facilitating solid predictive maintenance. To predict the life of systems and processes, a lot of data must be collected and processed so that only relevant data is extrapolated and analyzed.
2. Fault detection and isolation
Detecting and identifying system and process faults such as changing ambient temperatures, humidity, pressure differences and vibrations. This includes exactly where the faults occur. This essential part of predictive maintenance aims to reduce unexpected system failures.
3. Calculate time to failure
The analysis of condition and prediction of future downtime is referred to as Remaining Useful Life (RUL). This is also a crucial function for solid predictive maintenance. This uses historical data, real-time data and anomalous parameters to predict when maintenance, upgrade or repair is needed before unplanned downtime occurs.
4. Maintenance planning
By scheduling maintenance when it is actually needed, you optimize the use of resources and ensure that maintenance takes place at times when production is down anyway, such as after working hours, on weekends or at night. Servicing a train at night in the depot is easier than servicing a train that has stopped on a remote track during business hours.
5. Predictive maintenance IoT sensors
For good execution, there are IoT sensors and wireless gateways to receive real-time data about your machines and equipment.
6. Software for predictive maintenance
Collecting, structuring and analyzing the data is done through IoT software. This ensures that you always have an up-to-date overview of the status of your equipment.
Predictive Maintenance is used in many different industries. Companies that rely on complex machinery and equipment benefit significantly from predictive maintenance to ensure operational efficiency and reliability.
Back in the 1940s, predictive maintenance was used by the U.S. railroads to detect fuel and coolant leaks in engines. Today, predictive maintenance continues to be used worldwide in the rail industry to minimize downtime.
Predictive maintenance is also widely used in the food industry, aviation industry, chemical industry, casting industry, wood industry, energy, transportation, mining, paper industry and healthcare and transportation to optimize production lines.
Offshore drilling rigs rely heavily on predictive maintenance because much of their equipment operates underwater and visual inspection is difficult. These companies gain reliable insight into systems and equipment life by analyzing and collecting big data.
Automation and predictive maintenance are two pillars of smart factories. Neither would be possible without IoT. It makes management easier, communication effortless and system analysis accessible to anyone with access to the enterprise platform.
The biggest advantage of combining predictive maintenance with IoT sensors is that systems and processes can be monitored remotely. You can check the status via your smartphone or laptop and receive instant notifications about anomalies or if something goes wrong. By setting up alerts via email, text message or push notifications you are immediately alerted to critical and dangerous situations so that appropriate measures can be taken.
IoT-based maintenance reduces costs, accelerates response time, improves safety and enables further automation of production processes. It is an integral part of the "Industrial Internet of Things" (IIoT) and plays a crucial role in the development of "smart factories.
Do you have a question about implementing predictive maintenance in your company? Our specialist guide you with your choice and installation.
Sensor Partners has specialized in non-contact measurement and detection for 32 years.
Our team bridges the gap between traditional business issues and intelligent sensor solutions. Our technical experience has helped us create a predictive maintenance system, helping our customers with sustainable business management.
Customization: We have partnerships with leading organizations to ensure that you can build a modular solution tailored to your specific needs.
We offer complete solutions, including a wide range of wireless sensors, gateways, cloud computing and an app for total visibility and understanding of your equipment. We pride ourselves on providing robust solutions that are easy to install, simple to use and require little to no maintenance.
We offer with the Neuron sensors of El-Watch more than 50 different sensors:
Vibration sensors
Temperature sensors
Different IP21 and IP67
Infrared temperature sensors
Different PT100 sensors
Pressure sensors
Various pressure sensors from 1 bar to 250+ bar
Various differential pressure sensors 500Pa-7500Pa
Vacuum sensors
Humidity sensors
Amp sensors
Various ampere sensors from 10A to 500A
Cabinet safety sensors
Water Detection Sensors
Dry contact sensors
Hourly sensors
Digitizers allow traditional wired sensors to be made wireless.
mA digitizers (normal and precision)
VDC digitizer and mV digitizer
Predictive maintenance can help you manage maintenance more efficiently. Keep in mind, however, that not all businesses require the same level of equipment reliability. A good starting point for evaluating your business is to look at essential requirements and maintenance program development.
Ask yourself the following questions:
Do you have a question about implementing predictive maintenance in your company? Our specialist guide you with your choice and installation.
We provide everything you need to get started. It is helpful to understand how the implementation process works and what is required. Here are our tips for preparing your business for predictive maintenance.
Yes, predictive maintenance is worth the investment! The exact benefits of predictive maintenance depend on the industry or even the specific processes to which it is applied. According to Deloitte's predictive maintenance research*:
The benefits of predictive maintenance increase with underlying maintenance costs. The higher the costs caused by failures, the greater the benefits. Companies can save money and improve reliability by addressing problems before they escalate.
Also, the systems and processes more sustainable. By preventing unexpected equipment failure, reducing unplanned downtime, lowering maintenance costs and increasing the overall efficiency of production lines, equipment life is extended and the Co2 footprint reduced.
The qualitative benefits:
The pillars of predictive maintenance include:
There are several types of predictive maintenance, including:
The more data is available about possible failures the better the predictions will be.
Data is the fuel for any predictive maintenance system. Data quality and data quantity are the essential factor for root cause analysis and early failure prediction. Therefore, increasing data quality and data coverage is a key challenge for any predictive maintenance program. The more information is available about predictable operational outages, the more accurate the predictions become.
We distinguish between necessary data - without which predictive maintenance cannot be applied - and additional data, which improves the quality of predictions.
Recording process failures with exact date and time is a fundamental requirement. In addition, process and component variables such as temperature, pressure, voltage and other physical measurements are necessary to identify the causes of failures.
Indirect process parameters, such as raw materials, supplier information and assigned employees, can support analysis. Additional sensors, such as laser sensors, can be integrated and connected to monitor important process parameters, such as shape or rotational speed.
Predictive maintenance is an investment, which also highlights the second key challenge: Setting up the necessary processes comes at a cost first. Companies must add IoT sensors to their machines and set up a wide range of IT infrastructure, processes and trained personnel.
Data from various sources must be integrated and transformed so that it can be made available on an appropriate platform. Dashboards, push notifications, and alert systems via email or text message must be set up to coordinate maintenance. The expertise of process experts and data scientists is needed to build and maintain a functional predictive model. In addition, staff must be trained to manage information influences and correctly interpret alerts.
In the context of the industry 4.0 - with increasing interconnectedness and new opportunities to collect, process and analyze data - predictive maintenance is a very powerful strategy.
The predictive maintenance strategy involves several steps: identifying critical assets, implementing sensors to monitor the health of these assets, collecting and analyzing data to detect anomalies, and planning maintenance activities based on the insights gained from the data analysis.
By knowing well in advance when a machine will fail, predictive maintenance prevents damaged equipment. It is based on advanced analytics and is a new way to organize and perform maintenance on an industrial scale.
Do you have a question about this topic? Our specialist will be happy to help you.
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