How The Drone Industry Evolve In 2018

Drones in 2018

Drones are a perfect example of how our technology has evolved and will continue to grow in the future. So, what exactly does it offer? For casual users, it is just a fun toy. However, drones have use cases in multiple fields, including safety, health, and industry.

Until now, drones have gone on a wild ride. It has been massively regulated by U.S. government considering the threats that it brings including spying. Moreover, drone safety had been a significant issue. But to completely understand drones, we need to understand both its advantages and disadvantages before making a final call on how they should be regulated.

For starters, there are good drones and destructive drones. Those drones that are created for good are being used to save lives, help improve business efficiency and use autonomous control so that the human limitation can be removed. Destructive or bad drones do the exact opposite and are harmful in one way or another.

In this article, I will try to understand the future of drones from the perspective of tech, security, and innovation.

Drone Technology

Drones have evolved rapidly in the last decade or so. However, drones are not new and existed from the 1930s. The modern era of drones started after 2001, and it is now slowly becoming part of our daily life. You can now own a drone only if your state laws allow you to do so.

So, what is next for drones? The answer depends on how artificial intelligence (A.I) can be used to make drones more useful. A.I. can help drones overcome the human limitation and make drones more useful in different sectors including industry, tech, delivery and so on. However, A.I. powered drones can also be used to kill humans, deliver drugs or worse spy on someone.

There has already been a lot of debate on how A.I. can provide drones with ultimate power. Elon Musk also called out on the ban on killer robots which can also include drones. But, we have to weigh the gains over a possible loss and should be used to improve the different levels of transportation, accessibility, and overall growth.

Drones can now be programmed to learn from mistakes. Learn to Fly by Crashing, a paper published by Carnegie Mellon University explores how AR drones 2.0  are learning by learning from their own mistakes. This is just one use case of how A.I. and big data are going to impact drones in future.

Another critical use case of drones that are going to evolve and get implemented is their use in the industry. Many big companies will use drones to inspect their infrastructure and send direct reports to the system. Furthermore, they are also equally useful for surveillance purposes. The delivery system will continue to be refined in future as currently, Amazon is testing the use of drones to improve the delivery system. Jeff Bezos, Founder & CEO of Amazon, says that the aim is to get items delivered in “60 minutes.

Regulatory Reform

Government regulations on drones have always been tight. However, 2018 can be the year when the laws become less stringent. The current regulations from Federal Aviation Administration (FAA) regulate drones such that they must be used within human sight. However, this regulation is binding at many levels as it will not allow commercial companies such as Amazon to use drones in their operation.

In 2017, the general perception towards drones changed as well as their purpose as such in recent natural disasters, fortifying their benefits. Now that change is progressing; the FAA needs to take note and began to set appropriate regulations for the future of this industry. Drones manufacturers will also play a role, by ensuring that their drones can navigate safely through an environment, especially rooftops, humans, vehicles, etc. To achieve this, current technologies need to be adapted to allow for autonomous navigation and self-learning capabilities.

Final Thoughts

Drones have evolved remarkably in the last decade or two. The commercial usage will only propel it towards mass adoption. 2017 has been the year where it is now impossible to ignore the benefit in natural disasters management and commercial spaces. With relaxation in regulations, more and more industries will adopt drones.

One more thing is how blockchain can be used to revolutionize drones. Recently, blockchain is impacting almost every industry. Drones can be the next thing that can utilize blockchain technology. We only have to wait and see if blockchain powered drones can become more secure, accurate and easily regulatable.

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Industry 4.0: Predictive maintenance use cases in detail

Predictive maintenance involves collecting and evaluating data from your machines to increase efficiency and optimize maintenance processes. Not only can you gage the condition of your equipment, but also more accurately predict when maintenance work is needed.

Industry 4.0: Predictive maintenance for milling machines

Spindles in milling machines are prone to breaking during the production process. What’s more, repairing spindles can be very expensive. Therefore, being able to predict damage and precisely when the spindle will break can greatly reduce costs.

To overcome this challenge, special sensors (e.g. ultrasonic or vibration sensors) identify the patterns of a fragile spindle. Relevant alert settings for the current state of the machine can then be created.

Milling machine working on a piec eof metal. Source: ©fotolia/Andrey Armyagov

The sensors generate data which is then compared to the information from the machine and the specific workpiece being processed. By analyzing the data, it is possible to identify patterns of behavior that more accurately predict when the spindle is about to break. This enables maintenance schedules to be planned accordingly.

The benefits in detail:

  • Higher process transparency
  • Lower maintenance costs
  • Reduced machine downtime

Industry 4.0: Predictive maintenance for heat exchangers

Deposits in the conduits can cause heat exchangers to clog. A further complicating factor is the fact that it is impossible to measure the flow rate of a heat exchanger directly. A complete blockage can cause serious problems, resulting in manufacturing errors and hours of downtime.

Close-up of a heat exchanger. Source: © Fotolia/missisya

One solution to this issue is to measure the temperature differential upstream and downstream of the heat exchanger. After gathering and visualizing the measured values, it is possible to define threshold values. These values can then be input into an alert system to notify employees as soon as the first signs of clogging appear.

The benefits in detail:

  • Early warning of anomalies indicating potential blockages
  • Reduced machine downtime and less wastage of materials

Industry 4.0: Predictive maintenance for the health of robots

It is difficult to plan robot maintenance if the health of a robot is monitored only locally or not at all. But why refrain from gathering relevant machine data? Many parameters can be monitored, including CPU and housing temperature as well as positioning and overload errors. By collecting and displaying this data centrally and then evaluating it, maintenance can be planned before the situation becomes acute.

Close-up of a robot's arm. Source: ©Fotolia/Sved Oliver

The benefits in detail:

  • Awareness of the health of the machine
  • Intervention before the machine is damaged
  • Increased uptime
  • Early recognition of wear

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Oracle IoT Cloud for Industry 4.0 Helps Organizations Make Process Improvements for More Intelligent Supply Chains

Oracle IoT Cloud for Industry 4.0 Helps Organizations Make Process Improvements for More Intelligent Supply Chains

Oracle IoT Cloud for Industry 4.0 Helps Organizations Make Process Improvements for More Intelligent Supply Chains

New Augmented Reality, Machine Vision, Digital Twin and Automated Data Science capabilities enhance production, logistics, warehousing and maintenance.

Empowering modern businesses to improve production intelligence and market responsiveness, Oracle today unveiled new Industry 4.0 capabilities for Oracle Internet of Things (IoT) Cloud.

The advanced monitoring and analytics capabilities of the new offering enables organizations to improve efficiency, reduce costs, and identify new sources of revenue through advanced tracking of assets, workers, and vehicles; real-time issue detection; and predictive analytics.

According to The Economist Intelligence Unit, 63 percent of manufacturers have either undergone substantial digital transformation or are in the process of transforming parts of their organization, and 19 percent are developing transformation strategies. To remain competitive in the modern economy, businesses need to leverage new technologies and data to modernize their supply chains and improve visibility, predictive insights, and automation through connected workflows.

With new augmented reality, machine vision, digital twin and data science capabilities, Oracle IoT Cloud enables organizations to gain rich insight into the performance of assets, machines, workers, and vehicles so they can optimize their supply chain, manufacturing, and logistics, reduce time to market for new products; and enable new business models.

Bhagat Nainani, group vice president, IoT Applications at Oracle, said:

“IoT is the great enabler of Industry 4.0’s potential, providing real-time visibility and responsiveness at every step of the production process – from raw materials to customer fulfillment.”

“Oracle empowers organizations to create smart factories and modern supply chains with seamless interaction models between business applications and physical equipment. By receiving real-time data streams enhanced with predictive insights, our IoT applications provide intelligent business processes that deliver quick ROI.”

Today’s expansion follows the recent announcement of artificial Intelligence, digital thread and digital twin for supply chain, as well as industry-specific solutions for Oracle IoT Cloud. Oracle IoT Cloud is offered both as Software-as-a-Service (SaaS) applications, as well as Platform-as-a-Service (PaaS) offerings, enabling a high degree of adaptability for even the most demanding implementations.

Scott Rogers, technical director at Noble Plastics, said:

“We plan to leverage Oracle IoT Cloud and its machine learning capabilities to automatically analyze information gathered from the robot and process-monitoring systems. These analytics could help Noble identify ways to reduce cycle time, improve the manufacturing process, enhance product quality, and cut downtime.”

Oracle plans to add the new capabilities across the entire range of IoT Cloud Applications – Asset Monitoring, Production Monitoring, Fleet Monitoring, Connected Worker, and Service Monitoring for Connected Assets:

  • Digital Twin: Enables remote users to monitor the health of assets and prevent failures before they occur, as well as running simulations of “what-if” scenarios in the context of the business processes. With Digital Twin, organizations have a new operational paradigm to interact with the physical world, allowing lower operational and capital expenditures, minimizing downtime, and optimizing asset performance.
  • Augmented Reality: Gives operators and plant managers the ability to view operational metrics and related equipment information in the context of the physical asset for faster troubleshooting and assisted maintenance. In addition, the use of AR in training technicians reduces errors and on-boarding time, and improves user productivity.
  • Machine Vision: Provides detailed non-intrusive visual inspections, which can detect defects invisible to the naked eye, at high speed and scale. Following the rapid inspection, Machine Vision sets in motion appropriate corrective actions when anomalies and errors are spotted.
  • Auto Data Science: Automated business-specific data science and artificial intelligence algorithms continuously analyze asset utilization, production yield and quantity, inventory, fleet performance, as well as worker safety concerns, to predict issues before they arise. Auto Data Science features enable users to see performance metrics of each step in the modern supply chain with the ability to drill down into specific issues at each location without employing an army of data scientists.

Oracle IoT Cloud enables companies to monitor capital intensive assets to reduce downtime and servicing costs, and track utilization for accurate lifecycle insights and asset depreciation data, which improves enterprise procurement efficiency. The rich pool of data created by sensors within products enables organizations to offer their products as a service, gain insight into how customers are using their products, and offer improved value-added services that drive new sources of revenue.

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Industry 4.0: Condition monitoring use cases in detail

Industry 4.0: Condition monitoring in laser welding processes

Getting operational data from a laser during the welding process is difficult. This is especially the case when the data appears only on the human-machine interface (HMI). What’s more, the HMI is not easily accessible if located on or between the lines. This can cause disruptions in a highly automated and connected laser line. In the event of unscheduled downtimes, the whole line may break down.

Close-up of a laser welding machine. Source: ©fotolia/Patrick Foto

To solve this problem, we decided to gather and process machine notifications centrally along with the operating times of different modules. This includes the temperature of the water used to cool to the laser diode. We then introduced a ticket system to assign specific tasks to the machine operators. In addition, we implemented an automatic escalation system, where the response depends on the severity the issue.

The benefits in detail:

  • Reduced machine downtime
  • Increased production output

Industry 4.0: Condition monitoring in the Drilling Laser Line

We also implemented condition monitoring in a Drilling Laser Line (DLL) on the shopfloor. This highly automated and interconnected line comprises a materials handling process, a laser process, a flow test, and a test station. The DLL itself, along with external metering equipment, normally records a lot of measurement data values. The DLL is serviced either according to a schedule or when it exceeds control or tolerance limits.

Machines standing on the shop floor of a factory. Source: Bosch

To evaluate the amount of soiling, we started to display all data centrally. We then evaluated the data to detect if there was a need for early servicing.

The benefits in detail:

  • Flexible and easy-to-plan service intervals to boost overall equipment effectiveness (OEE)
  • Reduced need for servicing

Industry 4.0: Condition monitoring of electrical boxes

Insurance providers demand that plant operators regularly check the status of electrical boxes to prevent fires. These checks are often conducted manually – a low-value and time-consuming job.

Woman electrician standing in font of a breaker box with a tablet computer in hand. Source: ©iStock/aydinmutlu

Using a simple sensor, we were able to read the thermal data from the electrical box. We then visualized this data and set up a basic alert and ticket system in the event of deviations.

The benefits in detail:

  • Automation of the safety control process
  • Time saved on checking and improving safety

Industry 4.0: Condition monitoring when working with spindles

Spindles are used in various ways to process materials in production. However, obtaining data from this component can be challenging – especially when the machine is older. The result is that maintenance often follows pre-set cycles.

Close-up of a spindle. Source: ©Fotolia/sorapolujjin

With the help of our project partner, we read and visualized the spindle tension and speed using an OPC DA connector. We then evaluated the data, which allowed us to send push notifications to employees when deviations occurred.

The benefits in detail:

  • Reduced downtime thanks to swift intervention in the event of a disruption
  • Increased savings

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Industry 4.0: 10 use cases for software in connected manufacturing

1. Monitoring lubricants & filters in hydraulic valve production in Industry 4.0

The cleanliness of the oil used to test hydraulic valves after production must meet a specified ISO target code. In order to obtain valid test results, we had to improve the oil quality.

Man sitting next to a machine with a laptop uin his hands. Source: Bosch Rexroth

We retrofitted the test benches, with IoT gateways and connected sensors. This enabled existing machines to communicate and perform sensor-based monitoring of the testing medium. Together with our customer, we created a rule-based method to monitor the ISO cleanliness level of the oil. It is now possible to continuously monitor the processing units and use an automated system for maintenance tickets.

With this approach, our customer was able to transform a manual process into an automated process.

The benefits in detail:

  • Lower maintenance costs
  • Less complex manual testing
  • Increase in overall equipment effectiveness (OEE)

2. Quality management of the pressing process

Airbag control units are manufactured by mechatronic presses that assemble every component by mating. To gain a better understanding of these mating processes and how process parameters and product quality relate, we extracted the process data from the proprietary press control system. That way we were able to monitor and demonstrate the force and position of the pressing processes.

Production worker performing a quality check Source: ©fotolia/Kzenon

We used the data to define a template process which then served as a reference for each press in the production. This allowed a direct evaluation of every single pressing process, based on the raw data. In the past this was only possible with a downstream quality assessment.

The benefits in detail:

  • More process transparency
  • Increase in product quality

3. Harmonization of tightening processes

When production lines are scattered across the world, it is important that products should be of the same quality, regardless where they are manufactured. One of our customers faced this issue when it came to improving the overall quality of tightening processes.

A Bosch Rexroth worker using a nur runner. Source: Bosch Rexroth

Our approach in this Industry 4.0 project was to connect the nut runners in different production locations to centralized software. This made it possible to apply the same quality standards to processes in different locations.

The benefits in detail:

  • More process transparency
  • Better product quality

4. Centralized monitoring & automated ticket allocation

In this Industry 4.0 use case, we developed a centralized monitoring solution in collaboration with a manufacturer. Because there was no central management system for the machines, error messages were only displayed locally on the human-machine interface (HMI). This meant that workers had to wait by the machine and might overlook other important issues.

Close-up of an OSRAM worker holding his Ticket Manager. Source: Offenblende

We decided to set up one centralized system to record and display all machine data. We also established a system that automatically creates maintenance tickets and allocates workers depending on the situation. The workers access this system using an Android app with push notifications.

The benefits in detail:

  • An optimized maintenance process
  • Cutting the cost of failures by implementing a transparent and consistent troubleshooting process

5. EDM cycle time monitoring

The challenge here related to the monitoring of an electrical discharge machining (EDM) process: The data on process parameters were stored in a database which was checked only once a day. We wanted to evaluate the data using a manufacturing execution system (MES). But since the machines were old, the expense of connecting them to the MES was not a cost-effective option.

bosch plan homburg Source: Bosch

Instead, we used a software connector to read out and display the data directly from the machine. That way we were able to evaluate the status and notify associates when deviations occurred. This also allowed us to check and service machines on demand.

The benefits in detail:

  • Early detection of cycle time deviations
  • Increase in output

6. Cycle time monitoring of CNC machines

Monitoring the status of a CNC machine is particularly difficult. One of the biggest challenges which many large manufacturing facilities are facing is not being able to see what is going on at a granular level. In other words, monitoring is labor-intensive and often only provides limited insight into operations. Employees normally check the vibrations of the machines at predefined intervals. This fixed schedule means that a problem may go undetected for some time. It is also hard to calculate the cycle time of various machines and pinpoint where underperforming machines are affecting production.

Close-up of a CNC machine. Source: ©Fotolia/prescott09

We decided to connect sensors to each machine to collect information on its operating status and performance. These parameters were then centrally processed and displayed. This allowed us to analyze information in close to real time. We were thus able to trigger standardized machine checks to optimize production parameters.

The benefits in detail:

  • Reduced machine downtime
  • Fault prevention thanks to an early warning system
  • Continuous improvement based on machine benchmarking and condition monitoring

7. Condition monitoring of cooling systems

Blocked pipes in a cooling system can lead to pump failure. We encountered this problem in a plant where there was one cooling system with four cooling pumps. There was no central monitoring of cooling performance over time.

Photo of a cooling system standing on the shop floor. Source: ©depositphotos/tomasz_parys

We attached temperature and flow sensors to the cooling pipes to generate data. Then we defined limits for cooling power and flow.

The benefits in detail:

  • Advance warning of pipe blockages
  • Less need for pump maintenance
  • Less plant downtime

8. Condition monitoring of cutting fluids

Managing cutting fluid involves measuring the concentration, dosage and addition of fluid, and documenting measurements. It can be a completely manual process which is not always ideal.

Close-up of a milling machine. Source: ©Fotolia/Kamdy

Using a digital refractometer, we were able to measure the concentration of cutting fluid. If air bubbles formed in the cutting fluid mixture, this rendered the measured values useless. These values then had to be deleted.

The benefits in detail:

  • Long-term documentation, which can be used to improve tool life and surface finish quality
  • Cost savings resulting from lower dosing and consumption of water-soluble cutting fluids
  • Compliance with cutting fluid tolerance

9. Product quality monitoring in paint shop

Painting can be a demanding task. Especially when it comes to complex-shaped items like car windshield wiper arms. This means that a paint shop has to deal with a varying degree of quality when it comes to their paint jobs.

Photot of the shop floor of a paint shop. Source: Bosch Rexroth

By integrating the IoT gateway into the process, we enabled our project partner to collect production data. This helped identify three parameters of relevance to product quality: temperature, humidity and paint consumption. We then defined threshold values enabling alerts to be issued when quality parameters were out of spec.

The benefits in detail:

  • Faster response time in case of quality deviations
  • Better product quality

10. Vibration monitoring for milling machines

Another Industry 4.0 use case is vibration monitoring in milling machines. By positioning sensors close to the machine it is possible to measure vibration and gather information about specific vibration patterns during cutting operations like milling or drilling. In this way process data is collected on a large scale and provides a unique “digital fingerprint” for every milling process. By comparing the measured roughness with the individual fingerprints you can see how the two sets of data correlate.

Close-up of the spindle of a milling machine. Source: ©fotolia/Andrey Armyagov

The benefits in detail:

  • Alert system when milling process gets out if spec
  • Quick response time should a problem occur

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