#Industrial IoT Sensors

[lansitec]

Smart Helmet Tracker with Sensors for Construction Accident Prevention

Construction workers are constantly exposed to a variety of risks, making the business high-risk by nature. Our top priority has always been making sure these workers are safe. Smart Helmet Sensors with cutting-edge technology have become a game-changer in reducing accidents and improving safety protocols on building sites in recent years. These cutting-edge Helmet Sensors dramatically lower the risks that construction workers encounter by providing real-time monitoring and data collection.

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[ai-innova7ions]

Video Autogenerated by Faceless.Video

Micro AI is revolutionizing the way we interact with technology.

Micro AI is transforming our interaction with technology by providing lightweight, hyper-efficient models tailored for Edge devices such as smartwatches, IoT sensors, drones, and home appliances. This cutting-edge innovation facilitates real-time data processing and decision-making directly on the device, eliminating reliance on constant cloud connectivity. Imagine your smartwatch instantly analyzing health data or your smart home system making immediate adjustments based on real-time inputs—all thanks to micro AI. One of the key benefits of micro AI lies in its low latency and local processing capabilities. In industrial automation, it can monitor machinery in real time to predict failures before they occur. For smart homes, it enhances convenience and security by allowing appliances to learn from user behavior while optimizing energy consumption. In healthcare, wearable devices equipped with micro AI can provide critical monitoring of vital signs and alert medical professionals during emergencies—ensuring timely interventions that could save lives.

#microai #EdgeComputing

[creativeera]

IoT in Manufacturing Market is Estimated to Witness High Growth Owing to Need for Streamlining Operations

The IoT in manufacturing market involves connecting and integrating physical devices, sensors, and other smart objects into the manufacturing operations through the use of networking and cloud technologies. It enables the collection of information from various manufacturing equipment and assets and utilizes analytics tools to analyze the collected data to optimize operations. IoT allows manufacturers to drive improvements in equipment performance, reduce downtime, improve quality control, and optimize logistics and inventory management. The advantages of IoT in manufacturing include increased productivity, predictive maintenance, asset monitoring, inventory management, and energy management. The need for streamlined operations, improved asset utilization, and reduced maintenance costs through advanced monitoring and predictive analytics is fueling the demand for IoT in manufacturing. The global IoT in manufacturing market size was valued at US$ 198.8 billion in 2022 and is anticipated to witness a compound annual growth rate (CAGR) of 18.4% from 2023 to 2030. Key Takeaways Key players operating in the IoT in Manufacturing are EnableX.io (VCLOUDX PTE. LTD), Twilio Inc., Infobip Ltd., Vonage Holdings Corp, M800 Limited, MessageBird BV, Iotum Inc., Plivo Inc., Voxbone SA, Snich AB, Telestax, Voximplant (Zingaya Inc.), Mitel Networks Corporation, 8x8 Inc., AT&T Inc., Voxvalley Technologies, Avaya Inc., Bandwidth Inc. (Bandwidth.com), Wazo Communication Inc., and IntelePeer Cloud Communications. These players are focusing on developing advanced IoT solutions and services for manufacturing applications. The key opportunities in the Iot In Manufacturing Market Forecast include predictive maintenance through condition monitoring of equipment, remote asset management through sensors and connectivity, improving supply chain visibility, and optimizing energy consumption. Adoption of advanced analytics is also opening up new revenue streams through data monetization. North America is expected to continue dominating the global IoT in manufacturing market during the forecast period owing to the presence of many global players and early adoption. However, Asia Pacific is expected to witness the highest growth attributed to increasing investments by governments and manufacturers in smart factory initiatives to drive industry 4.0. Countries like China, India, Japan, and South Korea are emerging as global manufacturing hubs and rapidly adopting IoT technologies. Market drivers The key driver fueling the growth of IoT in manufacturing is the need for streamlining operations through real-time data collection, monitoring, and analytics. IoT allows connecting all manufacturing assets and enables data-driven decision making for predictive maintenance, quality control, inventory management, and production planning. This helps reduce downtime, save costs, improve overall equipment effectiveness, and enhance operational efficiency. IoT also enables remote asset management and driving energy efficiency initiatives through connected smart systems and remote asset performance monitoring.

PEST Analysis Political: IoT in manufacturing market is affected by government regulations around data privacy and security. Stricter privacy laws make it challenging for businesses to collect and use customer data. On the other hand, regulations supporting industry digitization creates opportunities for IoT solutions. Economic: Factors like global economic growth, industrial production levels, and investment in automation impact demand for IoT systems in manufacturing. During recessionary periods, organizations may delay IoT deployments to control costs. Social: Younger workforce is more receptive to new technologies. Skill gaps challenge wider IoT adoption. Awareness programs help promote social acceptance of advanced manufacturing technologies. Technological: Emerging technologies like AI, 5G, edge computing, and blockchain offer new possibilities for optimizing manufacturing processes. However, integrating legacy systems with advanced IoT platforms poses technological challenges. Security also remains a key concern with expanding network connectivity. The geographical regions where the IoT in manufacturing market is concentrated in terms of value are North America and Europe. North America accounts for the largest share mainly due to early adoption of Industry 4.0 technologies by US factories. The Asia Pacific region is projected to be the fastest growing market during the forecast period. This is because key developing economies like China and India are making heavy investments to automate their manufacturing industries using industrial IoT solutions. China's "Made in China 2025" initiative advocates implementation of IoT, robotics, and other innovative technologies across manufacturing sectors.

Get more insights on Iot In Manufacturing Market

About Author:

Money Singh is a seasoned content writer with over four years of experience in the market research sector. Her expertise spans various industries, including food and beverages, biotechnology, chemical and materials, defense and aerospace, consumer goods, etc. (https://www.linkedin.com/in/money-singh-590844163)

[futureelectronic1527]

NXP: MCX A Series Launch Video

https://www.futureelectronics.com/resources/featured-products/nxp-mcx-n-mcx-a-microcontrollers . MCX A Series all-purpose microcontrollers (MCUs) address a wide range of applications with scalable device options, low power and intelligent peripherals. Designed to allow engineers to do more, the new MCX A series is optimized with the essential features, innovative power architecture and software compatibility required by many embedded applications. https://youtu.be/fjNG2t4TBXQ

[techninja]

Predictive Maintenance Precision: Insights from AI

AI-Based Predictive Maintenance

AI-based predictive maintenance is revolutionizing industries by leveraging artificial intelligence to forecast equipment failures before they occur, thereby minimizing downtime, reducing costs, and enhancing operational efficiency. In this article, we delve into the significance, workings, benefits, challenges, and future prospects of AI-based predictive maintenance.

Discover how AI-based predictive maintenance is revolutionizing industries by leveraging artificial intelligence to forecast equipment failures and optimize maintenance strategies.

1. Introduction to AI-Based Predictive Maintenance

Predictive maintenance involves the use of data and analytics to predict when equipment failure is likely to occur, allowing for timely maintenance and preventing unexpected breakdowns. With the integration of artificial intelligence (AI), predictive maintenance has become more accurate, efficient, and cost-effective.

2. Importance of Predictive Maintenance in Industries

Industries across various sectors rely on machinery and equipment to maintain productivity and meet customer demands. Unexpected equipment failures can lead to costly downtime, reduced output, and compromised safety. Predictive maintenance helps mitigate these risks by enabling proactive maintenance actions based on data-driven insights.

3. Understanding AI in Predictive Maintenance

How AI Revolutionizes Predictive Maintenance

AI algorithms analyze historical data patterns and real-time sensor data to predict equipment failures with high accuracy. These algorithms continually learn and adapt, improving prediction accuracy over time.

Applications of AI in Predictive Maintenance

AI is applied in various predictive maintenance tasks, including anomaly detection, fault diagnosis, remaining useful life prediction, and scheduling maintenance activities based on equipment condition and workload.

4. Key Components of AI-Based Predictive Maintenance Systems

Data Collection and Monitoring

Data from sensors, IoT devices, and equipment logs are collected and monitored in real-time to identify abnormalities and patterns indicative of potential failures.

Machine Learning Algorithms

Machine learning algorithms process the collected data to identify correlations, trends, and anomalies, enabling predictive modeling and decision-making.

Predictive Analytics

Predictive analytics techniques, such as regression analysis and time-series forecasting, are used to predict equipment failures and prescribe optimal maintenance actions.

5. Benefits of AI-Based Predictive Maintenance

Cost Savings

By preventing unplanned downtime and minimizing maintenance costs, AI-based predictive maintenance helps organizations save money and optimize resource allocation.

Increased Equipment Reliability

Regular maintenance based on predictive insights enhances equipment reliability, prolongs asset lifespan, and improves overall operational efficiency.

Enhanced Safety

Proactive maintenance reduces the risk of equipment failures and associated safety hazards, creating a safer work environment for employees.

6. Challenges and Limitations of AI in Predictive Maintenance

Data Quality and Availability

The effectiveness of AI-based predictive maintenance relies on the quality and availability of data. Incomplete or inaccurate data can lead to unreliable predictions and false alarms.

Implementation Costs

Initial investments in AI infrastructure, sensors, and data management systems may pose financial challenges for organizations, especially small and medium-sized enterprises.

Integration with Existing Systems

Integrating AI-based predictive maintenance systems with existing equipment and enterprise software requires careful planning and coordination to ensure compatibility and seamless operation.

7. Case Studies Highlighting Successful AI-Based Predictive Maintenance Implementations

Several industries, including manufacturing, healthcare, transportation, and energy, have successfully implemented AI-based predictive maintenance solutions, resulting in improved asset performance, reduced maintenance costs, and increased operational efficiency.

8. Future Trends and Innovations in AI-Based Predictive Maintenance

The future of AI-based predictive maintenance holds exciting possibilities, including advancements in predictive algorithms, integration with emerging technologies like edge computing and 5G, and the development of predictive maintenance-as-a-service offerings.

9. Conclusion

In conclusion, AI-based predictive maintenance offers a proactive approach to equipment maintenance, enabling organizations to optimize asset performance, reduce downtime, and enhance operational efficiency. While challenges exist, the benefits of AI in predictive maintenance far outweigh the costs, paving the way for a more reliable and sustainable future.

FAQs

What industries benefit most from AI-based predictive maintenance?

How does AI improve the accuracy of predictive maintenance?

What are the primary challenges in implementing AI-based predictive maintenance?

Can small businesses afford AI-based predictive maintenance solutions?

What role does data quality play in the effectiveness of predictive maintenance systems?

[chandni0867]

The Power of Predictive Maintenance Vibration Analysis Sensors

The integration of vibration sensors marks a significant leap forward in predictive maintenance. Wireless vibration sensors for predictive maintenance allow the seamless process of fault detection, swift responses, precision in analysis, the sophistication of machine learning, downtime reduction strategies, and the ultimate optimization of production efficiency.

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[macnman-techno]

What is the Internet of Things (IoT) development?

Industrial IoT, often abbreviated as IIoT, is all about applying Internet of Things technology in industrial settings, especially when it comes to equipping sensors and devices with cloud-based capabilities. If you want a great example of how this works, take a look at the Titan use case PDF.

In recent times, industries have been making use of machine-to-machine communication, which is commonly known as M2M, to achieve wireless automation and control. However, things have gotten even more exciting with the rise of cloud technology and its companions, like analytics and machine learning. Thanks to these advancements, industries can now add a new layer of automation, leading to the creation of fresh revenue streams and entirely new business models.

Some folks even call this whole concept of the fourth wave of the industrial revolution, or Industry 4.0. It's a game-changer.

Here are some typical ways we put IoT to work:

Smart Manufacturing: Making our factories smarter and more efficient.

Connected Assets and preventive & Predictive Maintenance: Keeping an eye on equipment and predicting when maintenance is needed.

Smart Power Grids: Making our energy systems more intelligent and responsive.

Smart Cities: Creating cities that are more connected and responsive to the needs of their residents.

Connected Logistics: Improving the way we transport goods and track their progress.

Smart Digital Supply Chains: Revolutionizing the way we manage the flow of goods and information throughout the supply chain.

[luckyonexcel]

Exploring the Future of IoT | Internet of Things

Internet of Things (IoT) has already transformed the way we perceive and interact with technology connecting everyday objects to the digital world. As we navigate through a rapidly evolving technological landscape it becomes crucial to delve deeper into the future of IoT and the endless possibilities it holds. Let’s explore the exciting advancements and emerging trends that will shape the future…

[electronalytics]

Battery-operated Remote Terminal Units Market Research, Size, Share, Analysis, Overview and Regional Outlook Study 2017 – 2032

The market for Remote Terminal Units (RTUs) that are battery-operated allows them to function in isolated or off-grid regions where access to a reliable power supply is restricted. RTUs are electronic devices used to monitor and manage remote equipment and processes in many different industries, such as oil and gas, water and wastewater management, power distribution, and telecommunications. Here is a summary of the market for battery-operated RTUs, including information on demand:

Market Overview: Due to a number of circumstances, the market for battery-operated RTUs has been expanding. The need for battery-operated RTUs has been fueled by the necessity for remote monitoring and control capabilities in various industries, particularly in places with weak power infrastructure. These devices enable efficient monitoring and control of remote assets without the requirement for a continual external power source thanks to their dependable and autonomous operation.

Demand Drivers:

1. Real-time monitoring and control of assets: situated in remote or isolating locations are necessary in many businesses. Without the requirement for a sizable power infrastructure or reliance on cable connections, battery-operated RTUs allow for effective data gathering, monitoring, and control of these assets.

2. Applications Off-Grid: In off-grid applications where access to a dependable power supply is restricted or nonexistent, battery-operated RTUs are widely used. Examples include remote water pumping stations in rural areas, environmental monitoring stations, remote weather monitoring stations, and remote oil and gas wellheads.

3. Emergency and Temporary Installations: Whereas immediate deployment is necessary, battery-powered RTUs are also used in emergency and temporary installations. When catastrophe recovery, building projects, or temporary infrastructure installations are taking place, these machines can instantly provide remote monitoring and control capabilities.

4. Environmental Monitoring: Data collection from remote weather stations, air quality sensors, and water quality sensors are all important aspects of environmental monitoring that battery-operated RTUs play a key role in. Continuous monitoring is made possible in remote or environmentally delicate locations by these equipment.

5. IoT Connectivity: The need for battery-operated RTUs has increased as the Internet of Things (IoT) is becoming more widely used in a variety of businesses. These devices are essential parts of IoT networks because they provide seamless data transmission, communication, and control between remote assets and central management systems.

Here are some of the key benefits for Stakeholders:

Remote Monitoring and Control

Flexibility and Mobility

Cost-Effective Deployment

Resilience to Power Outages

Integration with IoT and Smart Grids

Increased Data Accessibility

Environmental Monitoring and Compliance

Rapid Deployment for Emergency Response

Redundancy and Reliability

Scalability and Future-Proofing

We recommend referring our Stringent datalytics firm, industry publications, and websites that specialize in providing market reports. These sources often offer comprehensive analysis, market trends, growth forecasts, competitive landscape, and other valuable insights into this market.

By visiting our website or contacting us directly, you can explore the availability of specific reports related to this market. These reports often require a purchase or subscription, but we provide comprehensive and in-depth information that can be valuable for businesses, investors, and individuals interested in this market.

“Remember to look for recent reports to ensure you have the most current and relevant information.”

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Market Segmentations:

Global Battery-operated Remote Terminal Units Market: By Company • SIEMENS • SENECA • Omniflex • King Pigeon Communication Co.,Limited • Servelec Group • TEKBOX • Micro Sensor Co., Ltd. • Hitachi Group • Remsdaq Ltd Global Battery-operated Remote Terminal Units Market: By Type • GPRS • GSM • Modular • Others Global Battery-operated Remote Terminal Units Market: By Application • Power Industry • Government and Utilities • Industrial Global Battery-operated Remote Terminal Units Market: Regional Analysis The regional analysis of the global Battery-operated Remote Terminal Units market provides insights into the market's performance across different regions of the world. The analysis is based on recent and future trends and includes market forecast for the prediction period. The countries covered in the regional analysis of the Battery-operated Remote Terminal Units market report are as follows: North America: The North America region includes the U.S., Canada, and Mexico. The U.S. is the largest market for Battery-operated Remote Terminal Units in this region, followed by Canada and Mexico. The market growth in this region is primarily driven by the presence of key market players and the increasing demand for the product. Europe: The Europe region includes Germany, France, U.K., Russia, Italy, Spain, Turkey, Netherlands, Switzerland, Belgium, and Rest of Europe. Germany is the largest market for Battery-operated Remote Terminal Units in this region, followed by the U.K. and France. The market growth in this region is driven by the increasing demand for the product in the automotive and aerospace sectors. Asia-Pacific: TheAsia-Pacific region includes Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, China, Japan, India, South Korea, and Rest of Asia-Pacific. China is the largest market for Battery-operated Remote Terminal Units in this region, followed by Japan and India. The market growth in this region is driven by the increasing adoption of the product in various end-use industries, such as automotive, aerospace, and construction. Middle East and Africa: The Middle East and Africa region includes Saudi Arabia, U.A.E, South Africa, Egypt, Israel, and Rest of Middle East and Africa. The market growth in this region is driven by the increasing demand for the product in the aerospace and defense sectors. South America: The South America region includes Argentina, Brazil, and Rest of South America. Brazil is the largest market for Battery-operated Remote Terminal Units in this region, followed by Argentina. The market growth in this region is primarily driven by the increasing demand for the product in the automotive sector.

Visit Report Page for More Details: https://stringentdatalytics.com/reports/battery-operated-remote-terminal-units-market/10333/

Reasons to Purchase Battery-operated Remote Terminal Units Market Report:

• To obtain insights into industry trends and dynamics, including market size, growth rates, and important factors and difficulties. This study offers insightful information on these topics.

• To identify important participants and rivals: This research studies can assist companies in identifying key participants and rivals in their sector, along with their market share, business plans, and strengths and weaknesses.

• To comprehend consumer behaviour: these research studies can offer insightful information about customer behaviour, including preferences, spending patterns, and demographics.

• To assess market opportunities: These research studies can aid companies in assessing market chances, such as prospective new goods or services, fresh markets, and new trends.

In general, market research studies offer companies and organisations useful data that can aid in making decisions and maintaining competitiveness in their industry. They can offer a strong basis for decision-making, strategy formulation, and company planning.

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[norvisrilanka]

Arduino PLC | MQTT End Device | Industrial IoT device manufacturer | norvi.lk

How Programmable IoT Devices Operate

Having access to the most dependable and effective hardware speeds up the completion of your project. The ability to programme flexibly.

ESP32 Ethernet Device

When using ESP32 Ethernet, the NORVI ENET series is the best option because it has industrial-grade I/O and voltages. Both wireless and cable connectivity to the network are offered by ESP32 Ethernet. 

Industrial Arduino Mega

The NORVI Arita is an enhanced version of the NORVI Series. Five conventional variants with a choice of two potent microprocessors are offered. Arita is built to deliver all of the micro-controller's performance while maintaining reliability. It works with practically all industrial input and output formats. 

Arduino based Industrial Controller

Arduino IDE-programmable

Integrated OLED and customizable buttons for HMI

The ability to programme flexibly

LED signals for simple diagnosis

Applications Using a Programmable MQTT Device and Ultra Low Energy Batteries

Agent One Industrial Controllers are available for low power applications as well; STM32L series microcontroller-controlled devices are employed in ultra low power applications, where the devices must be powered by batteries for an extended period of time. When a device goes to sleep, the Agent One BT family is specifically built with transistor outputs to turn off external sensors.

Wall mount IoT Node

The NORVI SSN range is designed for independent installations in industrial settings with a focus on tracking sensor data or parameters from external devices. The implementations are made simple by the attachments for wall installation and pole mount. 

NORVI Controllers

Our Address :

ICONIC DEVICES PVT LTD

Phone : +94 41 226 1776  Phone : +94 77 111 1776

E-mail : info@icd.lk / support@icd.lk

Web : www.icd.lk

Distributors

USA

Harnesses Motion LLC

1660 Bramble Rd. Tecumseh, MI

49286, United States

Phone : +1 (734) 347-9115

E-mail : rgoldy@harnessedmotion.com

EUROPE

CarTFT.com e.K.

Hauffstraße 7

72762 Reutlingen

Deutschland

Phone : +49 7121 3878264

E-mail : sales@cartft.com MQTT End Device | Arduino PLC | Analog Input | Wireless sensor | ModBus MQTT gateway | Industrial IoT device manufacturer | WiFi Data logger

[thetejasamale]

IoT Sensor Market is Rising due to usage of sensors in IoT applications...

[svsembedded]

Indoor Temp, Humidity, Light, Air Quality Sensor Data Logging to Excel🖥️Using Arduinohttps://www.youtube.com/watch?v=AUI1-CqnQqQ

[businessintelligenceinsightsblog]

As per Business Intelligence Insights (BII) study, the Global IOT Sensors Market attained revenue growth of USD 8,210.50 million in 2021 and it is projected to reach around USD 58794.12 million by 2030, growing at a 27.90% CAGR.

[cimcondigitalpr]

Prognostic Solutions in Steel Plant and Tube Mill Industry

The global steel market reached a value of US$ 874.6 Billion in 2021. Looking forward, IMARC Group expects the market to reach a value of US$ 1,052.25 Billion by 2027, exhibiting a growth rate (CAGR) of 3.02% during 2022-2027. Significant growth in the automotive industry across the globe and Increasing oil & gas production owing to the transportation industry’s demand is creating a positive outlook for the market. But with growth comes a lot of challenges in the steel manufacturing process. Companies in the industry face major challenges with rapid digitalisation and unlocking potential in data, shifts in consumer demands, and the attraction of top talents.

To address these challenges and improve profitability, the largest integrated producer of engineering steels and specialized seamless tubes in the Asia Pacific region has the foresight to expand its market in the coming years. In recent years, the leadership team has placed a greater emphasis on better asset utilization and process optimization.

Steel Plant & Tube Mill Challenges:

Failure of AssetsComplex procedures and important resources are used to produce steel tubes. Production and product quality are both impacted by the unforeseen failure of assets. It is not practical to inspect equipment frequently due to the harsh steel mill environment and the risks that inspection of rotating equipment poses to operator safety.

Mills in poor condition A poorly maintained mill costs valuable time and scrap during setup and operation. The mill must be dependable so that the operator is not chasing mechanical problems during normal operation and setup. A good maintenance program, as well as rebuilds or upgrades when necessary, is essential.

Tube mill misalignment, poor mill condition, and inaccurate setup account for 95% of all problems in tube production. Most mills should be aligned at least once a year.

Low investment in technologyOne of the major problems which these industries face is the lack of incorporation of advanced technologies in Research and Development work in production. The same is the case for other sectors of the industry and has been the case for decades. One of the outcomes has been the relative unattractiveness of the sector vis-a-vis others among young engineers. With little research being done by the government or the private sector, Industries lack effective implementation of Industry 4.0 and technologies that can reduce risks and helps to cut overall production cost. 

Process monitoringSteel mills’ inspection and monitoring process usually follow specific procedures, requiring that workers operate in a typical analog way. Since it is a critical environment, stopping production to execute corrective actions is unfeasible, and the current method for monitoring these problems is not able to predict failures in the same way as intelligent predictive models do, thus demonstrating an inevitable gap to errors.

Lack of Talent and TrainingEven though the operators are skilled and trained to operate in tube mills, there are still some challenges and issues that they need to face every day. Tube mill owners should arrange training sessions on how to troubleshoot the problem and figure out what was causing the problem. If these problems are not addressed properly, aluminum fabrication, steel fabrication, or stainless-steel fabrication processes won’t have precise tubes and pipes to work with.

MaintenanceBesides the difficulty in performing the inspection itself because of the hostile environment of steel industries, the maintenance of the encountered problems is also difficult.

The corrosion of equipment and structures is also a common problem in these industries and the maintenance of these components is very complicated because of the hostile environment, as mentioned before. Besides, the diversity of complex processes in this industry also makes it difficult to understand the extracted data in the operation, and the improper management of this information in diffuse systems compromises the maintenance of the structures.

Because of this lack of precise management tools, failure prediction in the operation is not usually achievable, leading to unplanned shutdowns and the extensive use of corrective maintenance methods. 

Our Solution: 

With the advent of Industry 4.0, predictive maintenance has gained huge popularity in maintaining machine health. Predictive maintenance is aimed at reducing costly and unplanned downtimes and offers organizations to plan maintenance schedules in advance for increased efficiency. According to McKinsey, predictive maintenance typically reduces machine downtime by 30% to 50% and increases machine life by 20% to 40%.

CIMCON Digital’s VIBit is an intelligent, powerful, and compact sensor that monitors the conditions of machines or equipment and predicts their failures in advance using the latest AI/ML Technologies to avoid unplanned downtime saving time and money for the owners.

We set up VIBit, a platform for detecting and diagnosing machine anomalies so that we could track the real-time performance of crucial machinery and processes throughout the year. VIBit Tri-axial vibration, Temp, and audio sensors installed on rotary equipment transmit machine health data over Plant’s Wi-Fi. AI/ML algorithms on the edge platform provide early machine anomaly detection along with diagnostics and recommendations for corrective and preventive action.

It can be used in Steel Tube Plant Asset and Fault Diagnostics to identify faults like bearing failure, misalignment, and electrical faults in types of equipment like De-scalar: motor/pump, Fume extraction system: motor/pump, etc.

Benefits of setting up a VIBit:

Real-time monitoring of critical equipment’s health on a single dashboard.

Prioritize and plan maintenance action before catastrophic failure.

Reduce the risk of operator safety with remote monitoring.

Reduce planned downtime and eliminate unplanned downtime. 

Reduce process and quality-related issues.

Increase equipment efficiency and reduce carbon emissions.

In conclusion, the steel and metal industries are undergoing significant changes and it is important for manufacturers to adopt technologies that can assist in reducing manual labour, leveraging data, and improving asset health. By embracing the latest technological advances, steel and metal manufacturers can use predictive analytics to anticipate failures and proactively take action to reduce unplanned downtime and improve plant efficiency.