Predictive Maintenance in Manufacturing: What, Why, and How?

Bhagyashri Kambale | March 14, 2022
PREDICTIVE MAINTENANCE IN MANUFACTURING
Predictive maintenance analytics is a type of maintenance that frequently monitors an asset's health. This timely maintenance or monitoring of assets or machines reduces unexpected breakdowns and allows manufacturers to plan around their production schedule.

“Even the best-built machines need proper preventive maintenance to remain productive and reach their maximum working life.”

- PMI (PREVENTATIVE MAINTENANCE INSPECTION) 

Predictive maintenance methods and software have evolved. Companies no longer need to import data into spreadsheets and extract insights manually. Businesses can now effectively estimate maintenance tasks using AI and machine learning algorithms in predictive maintenance systems. Robots and Internet of Things (IoT) devices are also generating more data than ever before, helping manufacturers be more insightful in their operations and processes.


Why Should Predictive Maintenance Manufacturing Be Considered?

The depreciation cost is significant in industries like manufacturing, where the cost of advanced equipment is very high. This makes it important to make sure that the assets of the manufacturing company are well-managed.

Using the predictive maintenance model in these situations saves money on multiple levels. Even though there are protocols like lean management and six-sigma, their usefulness is being questioned when it comes to existing business practices.

In a world where technology dictates practically every aspect of our lives, it is essential to have efficient procedures powered by cutting-edge technology. In essence, predictive maintenance aims to upgrade asset management using IoT.

According to a PwC analysis, manufacturing predictive maintenance
  • Cuts cost by 12%
  • Increases uptime by 9%
  • Extends the life of old assets by 20%
  • Reduces safety, environmental, quality, and health hazards by up to 14%


Types of Predictive Maintenance Technologies

 
 
Vibrational Analysis Acoustical Analysis (sonic) Acoustical Analysis (ultrasonic) Infrared Analysis
This is the preferred method for predictive maintenance in high-rotational industrial plants.

It is cheaper than other condition monitoring methods because it has been around longer.

Vibrational analysis can detect imbalance, misalignment, and bearing wear in addition to looseness.
This form of analysis is employed for low-and high-rotating machines. It's popular among lubrication technicians.

However, it does not focus on identifying the reasons for rotating equipment failure by measuring and recording vibrations at discrete frequencies for trending purposes.

Instead, acoustic bearing analysis targets lubrication technicians and focuses on proactive lubrication.
Ultrasonic acoustical analysis is solely used for predictive maintenance.

Its ultrasonic detection capability can distinguish between ultrasonic noises of machine friction and stress.

This form of analysis is more accurate than vibration or oil analysis.
This form of analysis is not affected by an asset’s rotational speed or volume. As a result, it is ideal for a wide variety of asset types.

When the temperature is a good indicator of possible problems, infrared analysis is the most cost-effective way to keep things running smoothly before they break down.

It is frequently used to diagnose cooling, airflow, and even motor stress issues.


How to Apply Predictive Maintenance Analytics in Practice?

Management is supplied with ROI scenarios prior to implementing predictive maintenance on the factory floor. Additionally, maintenance personnel and machine operators require training on how to use PdM technology (predictive maintenance). Following this, the true implementation of predictive maintenance equipment begins.


Establish Benchmarks

The maintenance team establishes acceptable condition thresholds for sensor-equipped assets.


Connect Gadgets to the Internet of Things (IoT)

The sensor is attached to the asset. A vibration meter, for example, is attached to a mechanical asset through gears, while a temperature sensor is attached to a boiler.


Integrate Hardware and Software

In this case, the IoT device is connected to a central management system (CMMS) or a remote dashboard, which collects and analyzes data.


Establish a Maintenance Schedule

Inspections are initiated automatically by a CMMS when a condition limit is exceeded or manually by the person monitoring the dashboard.


Predictive Maintenance Example


Preventing Power Outages

Power outages can be extremely inconvenient for those affected. They can be discovered early and so avoided with predictive maintenance technologies. Sensors would once again be used in this situation to deliver artificial intelligence-based insight into assets. This intelligence-based insight alerts the plant supervisor when equipment is about to malfunction.


Manufacturing Supervision

Since industrial plants typically contain many expensive assets and valuable equipment, they may invest in infrared imagers to monitor various elements of assets, such as temperature, to avoid overheating. This predictive maintenance technology assists plants in avoiding excessive use of critical equipment, which might result in disruptive breakdowns.


Final Word

Predictive maintenance is an advantageous tool for larger organizations that have outgrown typical preventative maintenance approaches and have an additional budget. It can generate a positive return on investment, transforming the maintenance department into a source of cost savings and increased revenues.

Predictive maintenance has some drawbacks, such as high startup costs and the requirement for specialized expertise. However, it helps to conduct maintenance only when necessary, assisting facilities in cutting costs, saving time, and maximizing resources.

Consultation with equipment makers and experts in condition monitoring should be conducted prior to determining whether predictive maintenance is the best approach for specific assets.


FAQ


Is predictive maintenance cost-effective?

Yes. Predictive maintenance saves between 8% and 12% compared to preventive maintenance and up to 40% compared to reactive maintenance, according to the U.S. Department of Energy. 


What is the difference between predictive and preventive maintenance?

Predictive maintenance saves money on labor and materials, whereas preventive maintenance is less expensive to undertake. Moreover, preventive maintenance is scheduled on a regular basis, whereas predictive maintenance is scheduled on an as-needed basis, depending on asset conditions.


What is TPM?

TPM, i.e., Total Productive Maintenance is a team-based method that focuses on proactive and preventative techniques to increase plant and equipment reliability.

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How Collaborative Robots Are Revolutionizing the Manufacturing Industry

Article | December 10, 2021

A new form of robot is entering manufacturing plants all around the globe. Instead of being locked away in their own work cell, collaborative robots work side by side with their human counterparts. Together, they form the manufacturing crew of the future. Collaborative robots, or cobots, are more flexible, easy to use, and safer than industrial robots. Instead of ending up abandoned in a corner, they are proving to be serious expansions of production capacity leading to better ways of creating superior quality products. 1.1 A New Breed of Bot Cobots are a new type of automation product with their own ISO standards for safety and usability. For a robot to qualify as a cobot, it has to be used for tasks of a collaborative nature while sharing all or part of its reach space with human operators. So it is not the product alone that classifies it as a cobot. Industrial robots must be expertly programmed for one specific job along the production line. 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Cobots can side with traditional machinery and additive manufacturing equipment, aided by artificial intelligence and cloud connectivity while embedded in a networked environment rich with smart sensors and mixed reality interfaces. 1.2 A Unique Blend of Benefits Because it is fairly straightforward to reprogram a cobot to various tasks, they are perfect for high-mix, low-volume work to meet the rising demand for ultra-customized products. They can also do multiple tasks in unison, such as alternatingly loading a machine and finishing parts from the previous cycle. Here are some other advantages in addition to flexibility: • Low investment. Cobots typically cost a fraction of the price of an industrial robot, but they offer much lower payload and reach. ROI is typically one to two years. • Safety. With rounded surfaces, force-limited joints, and advanced vision systems, cobots are exceptionally safe. This reduces the risk of injury due to impact, crushing, and pinching. Driverless transport systems are wheeled mobile robots that immediately halt when their lasers detect the presence of a nearby human being. • Accuracy. Cobots score well on accuracy with 0.1mm precision or well below that. While they do typically sacrifice speed, dual-mode cobots can be converted to fully-fledged tools of mass production that run at full speed in their own safeguarded space. • Easy to program. Many brands offer user-friendly programming interfaces from beginner to expert level. This reduces the need for continuous availability of expensive and scarce expertise while giving current employees an incentive to upskill. And because they can be deployed within hours, cobots can be leased for temporary projects. • Research. 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For example: • Complex assembly routines and handling of flexible components. • Large vehicle subassemblies contain many variable components and require more hand-eye coordination than one cobot can handle. Humans are needed to make sure everything lands in the right position while the cobot provides assistive muscle power. • Fashion, footwear, jewellery, art pieces, and other products where creation borders on artistry rather than mechanical assembly require the aesthetic eye of humans. People are also needed to spot aesthetic deficiencies in custom one-offs in order to correspond with customers before finishing the production batch. • While intelligent automation software can spot bottlenecks in efficiency, humans are required for creative problem solving and context-awareness to make decisions. 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Article | December 14, 2021

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How Business Intelligence Helped SKF (SvenskaKullagerfabriken) to Efficiently Plan Their Future Manufacturing SKF is a key supplier of bearings, seals, mechatronics, and lubrication systems globally. The company posses its headquarter in Sweden and has distributors in over 130 countries. Due to SKF's extensive worldwide reach and product diversity, they constantly need to forecast market size and demand for their products to modify their future manufacturing. Generally, SKF experts developed and kept their forecasts in traditional and intricate excel files. However, the efforts of maintaining and reconciling disparate studies were excessively high. As a result, SKF used require days to generate a simple demand prediction. Later, SKF integrated its business data assets into a single system by utilizing business intelligence in production. Following that, they could swiftly begin sharing their data and insights across multiple divisions within their firm. They are now able to aggregate demand estimation fast and does not face cross-departmental issues about data integrity for the vast number of product varieties they manufacture. This intelligent data management enabled SKF to plan their future production operations efficiently. Final Words Business intelligence in manufacturing makes a big difference in the organization's entire operations. Given the benefits of business intelligence in manufacturing, a growing number of manufacturers are implementing it in their operations. According to Mordor Intelligence, Business Intelligence (BI) Market was worth USD 20.516 billion in 2020 and is anticipated to reach USD 40.50 billion by 2026, growing at a 12% compound annual growth rate throughout the forecast period (2021-2026). Hence, we may say that the business intelligence is crucial for manufacturing and is booming, thanks to its enormous potential and the numerous benefits it provides to various businesses. FAQ Why is business intelligence so important in manufacturing? Organization intelligence may assist businesses in making better decisions by presenting current and past data within the context of their business. Analysts can use business intelligence to give performance and competitive benchmarking data to help the firm run more smoothly and efficiently. What value does BI add to manufacturing? Business intelligence solutions provide insight into sales, profit, and loss, raw material utilization and can usually assist you in optimizing resources to increase your return on investment. Understanding your cost-benefit analysis enables you to manage production costs, monitor processes, and improve value chain management. What is business intelligence's key objective? Business intelligence is helpful to assist corporate leaders, business managers, and other operational employees in making more informed business

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Top Electronics Manufacturing Trends to Watch in 2022

Article | October 13, 2021

The electronics manufacturing business is adopting new technologies to create smart electronics manufacturing products for its consumer base. Next-generation technologies are shaping the future of the manufacturing industry by enabling it to create technologically advanced and user-friendly products. Matt Mong, one of the manufacturing industry's leading professionals, stated in an interview with Media7, “Be Different. Don’t position your product in an existing category. Instead, create your category and make the competition irrelevant and obsolete.” – Matt Mong, VP Market Innovation and Project Business Evangelist at Adeaca. The year 2022 will be a year of advancement and development for the electronics manufacturing industry. So, manufacturers are eager to embrace new technologies and produce more innovative, more user-friendly goods that become part of consumers' daily lives and meet their needs. To make the manufacturing process manageable and deliver advanced products, we will look at the top five trends flourishing in the electronics manufacturing industry. Top Five Electronics Manufacturing Industry Trends Future manufacturing technologies are transforming the electronics manufacturing industry's processes and products. Let's look at the top electronics manufacturing industry trends for 2022, which will propel the sector to new heights of technological advancement. Utilizing the Benefits of the Internet of Things The Internet of Things is being used in both the manufacturing process and the products themselves. It enables electronic manufacturing products and processes to become more intelligent and performance-driven to fulfill business and customer needs. In electronics manufacturing, the Internet of Things (IoT) enables businesses to solve common production challenges such as product quality issues, changing demands, and a complex global supply chain. As a result, it increases productivity and efficiency while reducing human effort. Industrial units may gather and analyze real-time data and processes using IoT-based sensor systems. Additionally, it assists organizations in managing data and transforms traditional manufacturing into an intelligent manufacturing unit. Using an ERP System to Maintain the Company's Competitive Edge ERP (Enterprise Resource Planning) is a centralized management system for all operational and business activities. The software automates all manufacturing processes and enables the electronics manufacturing sector to achieve higher precision throughout the manufacturing process and product delivery. ERP has the potential to boost productivity, improve efficiency, decrease expenses, and increase profitability. ERP enables electronics manufacturers to forecast, plan, modify, and respond to changing market demands. By using an ERP system in your manufacturing unit, you may expand your business and increase revenue. Making Use of Big Data The electronics manufacturing industry benefits from the use of big data to make critical business decisions. It aids in the integration of previously isolated systems to provide a comprehensive view of industrial processes. It also automates data gathering and processing, allowing for more excellent knowledge of each system individually and collectively. Big data also assists manufacturers in discovering new information and identifying trends, allowing them to optimize operations, improve supply chain efficiency, and find variables that impact manufacturing quality, volume, or consistency. In addition, big data assists the electronics manufacturing industry in keeping up with the rapidly changing digital world. Using AR and VR to Create Consumer-friendly Goods AR and VR are future manufacturing technologies that are changing electronics manufacturing products and driving growth. Robotics is a crucial usage of virtual reality in electronics production. Manufacturers may use powerful virtual reality software to design goods. This implementation of virtual reality software reduces production errors and saves time and money. AR in electronics manufacturing allows product developers to generate interactive 3D views of new products before production. AR and VR are part of Industry 4.0, the digital revolution of conventional electronics production units. Adoption of 3D Printing on a Wide Scale One of the essential advantages of today's electronics 3D printing is that companies can quickly prototype PCBs and other electrical devices in-house. In addition, 3D printing has simplified the electronics manufacturing process, and it is currently being utilized to manufacture multilayer printed circuit boards. It uses material jetting technology to spray conductive and insulating inks onto the printing surface. Let's look at an example of an analogy that worked for Jinzhenyuan - The Electronic Technology Co. Ltd., managed by Mr. Huang Runyuan, Jinzhenyuan's General Manager, and based on the concept of Industry 4.0. (Reference: Forbes) Jinzhenyuan - The Electronic Technology Co. Ltd. Takes a Significant Step Forward with Industry 4.0 Jinzhenyuan - The Electronic Technology Co. Ltd., formed in 2012, sells its products globally. In addition, it manufactures cellphones, computers, cars, and a variety of other consumer electronics. Due to changing market needs, the firm planned to upgrade its production facility to industry 4.0 by the end of 2017 to participate in smart manufacturing. The company increased production efficiency, shortened production cycles, and cut costs due to the digital revolution. Today, Jinzhenyuan is regarded as a model of digital transformation in the community in which it works. Let’s observe the statistics for Jinzhenyuan following the deployment of Industry 4.0. 32% improvement in total production efficiency 33% cost reduction 41% decrease in R&D to production cycles 51% reduction in substandard parts rate – from 3,000 to 1,500 per million Final Words The electronics manufacturing sector is on the verge of a digital revolution that will improve the production process efficiency and cost-effectiveness. Many of the world's biggest firms, like Apple, Microsoft, Hitachi, and Saline lectronics, are developing future agile factories to keep up with the world's digital transformation. Future manufacturing technology will help your manufacturing company make the manufacturing process more efficient and boost the business revenue. FAQs What are the future electronics technologies? Smart grid solutions, wearable technology devices, prefabricated goods, the Internet of Things, and robots are some of the future electronics innovations that will propel the business forward. Is the supply chain benefiting from new technology trends? Yes, supply chain management benefits from smart technology as well. Trucks equipped with cutting-edge technologies can get real-time data on the weather and road conditions ahead of time. It contributes to the supply chain process's reduction of possible risks. Which manufacturers are implementing the industry 4.0 concept in their factories? Whirlpool, Siemens, Hirotec, Tesla, Bosch, and Ocado, among others, have turned their traditional factories into digitally smart ones that incorporate all of the cutting-edge technology necessary to improve and optimize the production process. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the future electronics technologies?", "acceptedAnswer": { "@type": "Answer", "text": "Smart grid solutions, wearable technology devices, prefabricated goods, the Internet of Things, and robots are some of the future electronics innovations that will propel the business forward." } },{ "@type": "Question", "name": "Is the supply chain benefiting from new technology trends?", "acceptedAnswer": { "@type": "Answer", "text": "Yes, supply chain management benefits from smart technology as well. Trucks equipped with cutting-edge technologies can get real-time data on the weather and road conditions ahead of time. 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Predictive Maintenance in Manufacturing: What, Why, and How?

Article | March 14, 2022

Predictive maintenance analytics is a type of maintenance that frequently monitors an asset's health. This timely maintenance or monitoring of assets or machines reduces unexpected breakdowns and allows manufacturers to plan around their production schedule. “Even the best-built machines need proper preventive maintenance to remain productive and reach their maximum working life.” - PMI (PREVENTATIVE MAINTENANCE INSPECTION) Predictive maintenance methods and software have evolved. Companies no longer need to import data into spreadsheets and extract insights manually. Businesses can now effectively estimate maintenance tasks using AI and machine learning algorithms in predictive maintenance systems. Robots and Internet of Things (IoT) devices are also generating more data than ever before, helping manufacturers be more insightful in their operations and processes. Why Should Predictive Maintenance Manufacturing Be Considered? The depreciation cost is significant in industries like manufacturing, where the cost of advanced equipment is very high. This makes it important to make sure that the assets of the manufacturing company are well-managed. Using the predictive maintenance model in these situations saves money on multiple levels. Even though there are protocols like lean management and six-sigma, their usefulness is being questioned when it comes to existing business practices. In a world where technology dictates practically every aspect of our lives, it is essential to have efficient procedures powered by cutting-edge technology. In essence, predictive maintenance aims to upgrade asset management using IoT. According to a PwC analysis, manufacturing predictive maintenance Cuts cost by 12% Increases uptime by 9% Extends the life of old assets by 20% Reduces safety, environmental, quality, and health hazards by up to 14% Types of Predictive Maintenance Technologies Vibrational Analysis Acoustical Analysis (sonic) Acoustical Analysis (ultrasonic) Infrared Analysis This is the preferred method for predictive maintenance in high-rotational industrial plants. It is cheaper than other condition monitoring methods because it has been around longer. Vibrational analysis can detect imbalance, misalignment, and bearing wear in addition to looseness. This form of analysis is employed for low-and high-rotating machines. It's popular among lubrication technicians. However, it does not focus on identifying the reasons for rotating equipment failure by measuring and recording vibrations at discrete frequencies for trending purposes. Instead, acoustic bearing analysis targets lubrication technicians and focuses on proactive lubrication. Ultrasonic acoustical analysis is solely used for predictive maintenance. Its ultrasonic detection capability can distinguish between ultrasonic noises of machine friction and stress. This form of analysis is more accurate than vibration or oil analysis. This form of analysis is not affected by an asset’s rotational speed or volume. As a result, it is ideal for a wide variety of asset types. When the temperature is a good indicator of possible problems, infrared analysis is the most cost-effective way to keep things running smoothly before they break down. It is frequently used to diagnose cooling, airflow, and even motor stress issues. How to Apply Predictive Maintenance Analytics in Practice? Management is supplied with ROI scenarios prior to implementing predictive maintenance on the factory floor. Additionally, maintenance personnel and machine operators require training on how to use PdM technology (predictive maintenance). Following this, the true implementation of predictive maintenance equipment begins. Establish Benchmarks The maintenance team establishes acceptable condition thresholds for sensor-equipped assets. Connect Gadgets to the Internet of Things (IoT) The sensor is attached to the asset. A vibration meter, for example, is attached to a mechanical asset through gears, while a temperature sensor is attached to a boiler. Integrate Hardware and Software In this case, the IoT device is connected to a central management system (CMMS) or a remote dashboard, which collects and analyzes data. Establish a Maintenance Schedule Inspections are initiated automatically by a CMMS when a condition limit is exceeded or manually by the person monitoring the dashboard. Predictive Maintenance Example Preventing Power Outages Power outages can be extremely inconvenient for those affected. They can be discovered early and so avoided with predictive maintenance technologies. Sensors would once again be used in this situation to deliver artificial intelligence-based insight into assets. This intelligence-based insight alerts the plant supervisor when equipment is about to malfunction. Manufacturing Supervision Since industrial plants typically contain many expensive assets and valuable equipment, they may invest in infrared imagers to monitor various elements of assets, such as temperature, to avoid overheating. This predictive maintenance technology assists plants in avoiding excessive use of critical equipment, which might result in disruptive breakdowns. Final Word Predictive maintenance is an advantageous tool for larger organizations that have outgrown typical preventative maintenance approaches and have an additional budget. It can generate a positive return on investment, transforming the maintenance department into a source of cost savings and increased revenues. Predictive maintenance has some drawbacks, such as high startup costs and the requirement for specialized expertise. However, it helps to conduct maintenance only when necessary, assisting facilities in cutting costs, saving time, and maximizing resources. Consultation with equipment makers and experts in condition monitoring should be conducted prior to determining whether predictive maintenance is the best approach for specific assets. FAQ Is predictive maintenance cost-effective? Yes. Predictive maintenance saves between 8% and 12% compared to preventive maintenance and up to 40% compared to reactive maintenance, according to the U.S. Department of Energy. What is the difference between predictive and preventive maintenance? Predictive maintenance saves money on labor and materials, whereas preventive maintenance is less expensive to undertake. Moreover, preventive maintenance is scheduled on a regular basis, whereas predictive maintenance is scheduled on an as-needed basis, depending on asset conditions. What is TPM? TPM, i.e., Total Productive Maintenance is a team-based method that focuses on proactive and preventative techniques to increase plant and equipment reliability.

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Columbia Tech

Columbia Tech provides engineering design, manufacturing, global fulfillment and aftermarket services to innovation leaders in the life-science, pharmaceutical, bio-discovery, alternative energy, semiconductor, power management, LED, medical device, data storage, defense, homeland security and digital and molecular imaging industries.

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