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Silogic Technology
Cambridge, United Kingdom
1-10 Employees
2022
We are using artificial intelligence to tell you what is going on with your wind turbines and predict their future.
Product
ARTIFICIAL INTELLIGENCE PREDICTIVE MAINTENANCE
... ARTIFICIAL INTELLIGENCE PREDICTIVE MAINTENANCE ...
IJssel Predictive Manitenance UK
Wrexham, United Kingdom
51-100 Employees
-
We are convinced that smart maintenance could save you money. We are your specialist in the field of proactive maintenance, lubrication and condition-dependent maintenance. We are also happy to help you with your lubrication maintenance, from stock management, periodic lubrication and lubrication analysis. That is why we are proud that IJssel is the exclusive partner of Noria, the institute in the field of lubrication best practices. We provide professionals with tools to collect and interpret data in a pure manner. IJssel is a knowledge company with business experts, engineers and technicians. We build, improve and maintain production processes based on a thorough knowledge of practice. IJssel sees optimal lubrication maintenance as an important part of building, improving and maintaining production processes.
Core business
About IJssel Predictive Maintenance
... IJssel Predictive Maintenance works from the conviction that prevention ...
Siana
Aarhus, Denmark
1-10 Employees
2019
At Siana, our predictive maintenance system excels in constantly monitoring the health of your machinery. Trust Siana to keep a watchful eye on your machinery, every moment of every day. Siana is designed to pinpoint and address these issues before they escalate, ensuring you avoid these unnecessary costs. Experience how our products & services, fusing advanced sensor technology with AI and robust security, to enhance operations daily. How does Siana prevent breakdowns and unplanned downtime? Get in touch with our friendly experts and get a sneak peek with a live demo.
Product
Siana - Autonomous Predictive Maintenance
... an intuitive plug'n'play service for autonomous predictive maintenance ...
L.P. Larson Corporation
Braintree, United States
11-50 Employees
1969
Larson Corporation is a complete predictive maintenance solution company. LP Larson engineers provide our customers with expertise at the machinery maintenance level to insure against unscheduled downtime and unexpected equipment failure. Providing consultative expertise from design/install, operate/maintain or ready to replace -- applying a combination of predictive, preventive and reactive maintenance activities will help extend equipment life and improve operational performance of your assets.
Core business
Predictive Maintenance Services
... Predictive Maintenance ...
Uptake Technologies
Chicago, United States
251-500 Employees
2014
At Uptake, we help companies translate underutilized data into insights that predict and prevent failures before they happen. Our products are simpler to use, easier to scale, and faster to return value than other solutions. Our products give you the knowledge you need. Uptake’s insights are actionable at the street level.”. Uptake plugs in work orders and sensor intelligence into your maintenance analytics platform. Uptake Fleet empowers your team to cut maintenance costs, increase uptime, and improve fuel efficiency. We promise to deliver real-time insights easily present streamline results delivered in an ultra-simple UI, so you can make better business decisions that translate to a healthier bottom line. Faster cost analysis for both production and maintenance through a centralized cost center.
Core business
Predictive Maintenance | Uptake
... Uptake powers predictive maintenance ...
Oklahoma Predictive Maintenance Users Group
Stillwater, United States
1-10 Employees
1992
OPMUG consists of individual and company sponsored members from many different industries including utilities, manufacturing, refineries, municipalities, government, service organizations, educational institutions, and leading predictive maintenance vendors. OPMUG is a non‐profit organization incorporated in the State of Oklahoma. We provide professionals opportunities to share and obtain first-hand knowledge about predictive maintenance. A membership with OPMUG gives you opportunities for networking and keeps you up-to-date on the latest techniques and methods. The Oklahoma Predictive Maintenance Users Group (OPMUG) was established in 1992 to provide maintenance professionals throughout Oklahoma, and the surrounding states, an opportunity to share and obtain first hand knowledge about predictive maintenance. We offer trainings and seminars throughout the year.
Core business
Oklahoma Predictive Maintenance Users Group (OPMUG)
... organization promoting predictive maintenance through trainings and ...
Master Melody
Munich, Germany
1-10 Employees
2020
Zusätzlich lassen sich die Ressourcen für die Instandhaltungsarbeiten wie Personal oder Ersatzteilmanagement mit dem Wissen darüber, wann welche Maschinen gewartet werden müssen, besser planen. Wir bieten Ihnen die perfekte Lösung je nach Digitalisierungsgrad Ihrer Maschinen oder Anlagen an. Durch ein einzigartiges Feedback System lernt die Technologie von Ihren Experten während des Einsatzes. Die Einbettung von Predictive Maintenance ist ein langes, teures und unsicheres Projekt? Die Master Melody® Technologie macht das Vorverarbeiten der Sensordaten überflüssig.
Core business
Master Melody® Technologie | Ready-to-use Predictive Maintenance
... Master Melody® Technologie | Ready-to-use Predictive Maintenance ...
ELTEC Elektronik
Mainz, Germany
11-50 Employees
1978
Product
Predictive Maintenance
... Predictive Maintenance | ELTEC Elektronik ...
Katulu GmbH
Hamburg, Germany
11-50 Employees
2018
From the very beginning Katulu assisted us very well in a very close partnership: From evaluation, conception, IIoT- and cloud-technologies to the implementation and operation. With Katulu, we have efficiently and successfully scaled our AI-driven use cases across our global manufacturing network. The Katulu platform helped us to solve fundamental problems in industrial AI, such as heterogeneous data, data privacy and generalization, that we couldn't solve with any other solution before. Katulu GmbH provided us with competent advice in this regard. Katulu supported us to create a scalable multi-tennant IIoT cloud platform for KSB products. Boost efficiency while staying in control of your data and trade secrets through Katulu. Global production cost savings through cross-site quality control synergies and improve your First Pass Yield (FPY). Reduction of waste in manufacturing processes through cross-company, inline quality control.
Product
Predictive Maintenance mit dezentraler KI
... Federated Learning für Predictive Maintenance kennen - von höherer ...
ELTEC Elektronik AG
Mainz, Germany
11-50 Employees
1978
Product
Predictive Maintenance
... Predictive Maintenance | ELTEC Elektronik ...
Technologies which have been searched by others and may be interesting for you:
Some interesting numbers and facts about your company results for Predictive Maintenance
| Country with most fitting companies | United States |
| Amount of fitting manufacturers | 2134 |
| Amount of suitable service providers | 2366 |
| Average amount of employees | 1-10 |
| Oldest suiting company | 1978 |
| Youngest suiting company | 2020 |
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Predictive maintenance is a proactive maintenance strategy employed to forecast equipment failures and address them before they occur, utilizing data analysis tools and techniques. This approach hinges on the continuous monitoring of equipment conditions through various sensors and data collection devices, analyzing the data in real-time to detect anomalies, trends, and patterns that may indicate impending failure. The foundational principle of predictive maintenance lies in its ability to predict when equipment maintenance should be performed based on actual conditions, rather than relying on scheduled maintenance intervals or reactive maintenance after a failure has occurred. By doing so, it significantly reduces downtime, increases equipment longevity, optimizes maintenance tasks, and ultimately leads to substantial cost savings and efficiency improvements. The application of predictive maintenance spans a wide range of industries, including manufacturing, aviation, and energy, where the health of critical machinery directly impacts operational efficiency and safety. The integration of advanced technologies such as machine learning, artificial intelligence, and the Internet of Things (IoT) has further enhanced the capability of predictive maintenance systems, enabling more accurate predictions and insights. As a result, the role of predictive maintenance is increasingly pivotal, offering a competitive edge to businesses through improved reliability, reduced operational costs, and enhanced decision-making regarding equipment management and investment.
1. Cost Efficiency:
Predictive maintenance significantly reduces maintenance costs by identifying potential issues before they escalate into major failures. This proactive approach saves money on repairs and parts, as it allows for the optimization of maintenance schedules and resources.
2. Increased Equipment Lifespan:
By continuously monitoring equipment condition and performance, predictive maintenance extends the lifespan of machinery. It helps avoid the wear and tear that can result from operating equipment with undetected faults, leading to more sustainable asset utilization.
3. Improved Safety:
This method enhances workplace safety by predicting and preventing equipment failures that could potentially endanger workers. By addressing issues before they lead to accidents, predictive maintenance creates a safer environment for employees.
4. Minimized Downtime:
Predictive maintenance allows for repairs to be scheduled at the most opportune times, minimizing unplanned downtime. This ensures higher productivity and efficiency, as equipment is more likely to be operational when needed.
5. Better Resource Allocation:
With the ability to predict when maintenance will be required, companies can better allocate their resources, including manpower and inventory. This strategic planning leads to more efficient operations and can significantly reduce stress on maintenance teams.
While evaluating the different suppliers make sure to check the following criteria:
1. Technology and Analytics Capabilities
Ensure the supplier offers advanced analytics capabilities, including machine learning and artificial intelligence, that can accurately predict equipment failures before they occur.
2. Integration Ease
The solution should easily integrate with your existing machinery and IT systems to enable seamless data flow and analysis.
3. Industry Experience
Prioritize suppliers with proven experience in your specific industry, as they'll understand your unique challenges and requirements.
4. Scalability
Consider whether the solution can scale with your business needs, supporting more equipment or different types of machinery as your operations grow.
5. Support and Training
Look for suppliers that provide comprehensive support and training to ensure your team can effectively use the predictive maintenance system.
6. Cost-effectiveness
Evaluate the total cost of ownership, including initial setup, subscription fees, and any additional costs for updates or support, to ensure it fits within your budget.
7. Security
Ensure the supplier’s solution adheres to the highest data security standards to protect sensitive information and comply with industry regulations.
Predictive maintenance is revolutionizing industries by optimizing equipment efficiency and minimizing downtime. In manufacturing, this approach is critical for ensuring that machinery operates at peak performance. By leveraging data analytics and machine learning, companies can predict equipment failures before they occur, reducing costly unplanned downtime and extending the lifespan of machinery. This not only improves productivity but also significantly lowers maintenance costs. In the realm of energy and utilities, predictive maintenance plays a pivotal role in the maintenance of critical infrastructure such as power lines, transformers, and turbines. By predicting potential failures, companies can prevent large-scale disruptions, ensuring a reliable supply of energy to consumers and industries alike. This proactive approach is essential for maintaining the integrity of the energy grid and supporting sustainable energy initiatives. The aviation industry also benefits greatly from predictive maintenance. By continuously monitoring the condition of aircraft components, airlines can predict potential failures and perform maintenance before issues arise, enhancing safety and reliability. This approach reduces flight delays and cancellations, improving passenger satisfaction and operational efficiency. Transportation and logistics companies employ predictive maintenance to ensure the reliability of their fleet. Whether it’s trucks, trains, or ships, being able to foresee and rectify potential issues before they lead to breakdowns can significantly enhance operational efficiency, reduce costs, and ensure timely delivery of goods. In summary, predictive maintenance is a powerful tool across various industries, from manufacturing to aviation, enabling businesses to operate more efficiently, safely, and cost-effectively. By adopting predictive maintenance strategies, companies can not only prevent equipment failures but also gain a competitive edge in their respective markets.
As of my last update, predictive maintenance technologies broadly occupy a Technology Readiness Level (TRL) ranging from 7 to 9, signaling their transition from demonstration in operational environments (TRL 7) to actual system proven in operational environments (TRL 9). This high level of readiness is attributed to several technical advancements and the integration of various sophisticated technologies. Innovations in sensor technology, for instance, have vastly improved the ability to monitor equipment conditions in real-time, capturing data that is both more accurate and granular. Furthermore, the advent and maturation of machine learning and artificial intelligence (AI) algorithms have significantly enhanced the capacity to analyze this data, identifying patterns and predicting failures with a precision that was previously unattainable. These algorithms can now learn from historical data to forecast equipment malfunctions before they occur, allowing for preemptive maintenance actions that minimize downtime and reduce costs. Additionally, the integration of Internet of Things (IoT) technology has facilitated the seamless collection and transmission of sensor data, ensuring that predictive maintenance systems can operate efficiently in a wide range of operational environments. The combination of these technical elements—advanced sensors, sophisticated AI algorithms, and robust IoT connectivity—underscore the high TRL of predictive maintenance, reflecting its proven effectiveness and reliability in real-world applications.
In the Short-Term, predictive maintenance is set to leverage enhanced IoT connectivity and real-time data analytics. Developments in edge computing will allow devices to process data on-site, leading to immediate maintenance actions and reduced downtime. Integration of AI-driven analytics will improve the accuracy of failure predictions, enabling more targeted maintenance schedules. This phase is characterized by the adoption of more sophisticated sensors and machine learning algorithms that can identify subtle patterns indicative of potential failures. Mid-Term advancements will focus on the integration of digital twins and augmented reality (AR) into predictive maintenance strategies. Digital twins, virtual replicas of physical assets, will provide deeper insights into equipment behavior under various conditions, further refining maintenance predictions. AR will empower technicians with real-time, hands-free information and guidance during repairs, enhancing efficiency and reducing human error. Additionally, the use of blockchain for secure, tamper-proof maintenance logs will ensure data integrity, facilitating trust in automated maintenance decisions. Long-Term, predictive maintenance is expected to evolve with the advent of autonomous maintenance robots and the extensive use of AI in decision-making processes. These robots will perform routine maintenance tasks and repairs, guided by AI algorithms that can predict failures with high accuracy months in advance. Integration with smart cities and industries will enable a seamless flow of information across platforms, creating a fully interconnected ecosystem where predictive maintenance strategies are optimized across various sectors. Advanced AI models, capable of self-improvement through continuous learning, will drive this phase, making predictive maintenance more effective and less reliant on human intervention.
Some interesting questions that has been asked about the results you have just received for Predictive Maintenance
What are related technologies to Predictive Maintenance?
Based on our calculations related technologies to Predictive Maintenance are Big Data, E-Health, Retail Tech, Artificial Intelligence & Machine Learning, E-Commerce
Who are Start-Ups in the field of Predictive Maintenance?
Start-Ups who are working in Predictive Maintenance are Master Melody
Which industries are mostly working on Predictive Maintenance?
The most represented industries which are working in Predictive Maintenance are IT, Software and Services, Other, Manufacturing, Automation, Machinery Manufacturing
How does ensun find these Predictive Maintenance Companies?
ensun uses an advanced search and ranking system capable of sifting through millions of companies and hundreds of millions of products and services to identify suitable matches. This is achieved by leveraging cutting-edge technologies, including Artificial Intelligence.
