Who Moved My Data? Part 2: Insourcing Condition Monitoring

In my previous blog on this topic, “Who Moved My Data? Outsourcing Condition Monitoring,” I established the case for condition-based monitoring of critical assets to ensure a reduction in unplanned downtime. I also explored the advantages and disadvantages of outsourcing condition monitoring from critical assets. Here I discuss the do-it-yourself (DIY) approach to condition monitoring and explore its advantages and disadvantages.

Understanding the DIY approach

Now, let me be clear to avoid any confusion, when I refer to “do it yourself,” I don’t mean literally doing it yourself. Instead, this is something you own and customize to fit your applications. It may require a fair amount of input from your maintenance teams and plants. It’s not a one-day job, of course, but an ongoing initiative to help improve productivity and have continuous improvements throughout the plant.

Advantages and disadvantages of DIY condition monitoring (insourcing)

Implementing the solutions for continuous condition monitoring of critical assets by yourself has many advantages, along with some disadvantages. Let’s review them.

Advantages of insourcing (DIY) condition monitoring:

    1. Data ownership: One of the greatest benefits or advantages of implementing the DIY approach to condition monitoring is the control it gives you over data. You decide where the data lives, how it is used, and who has access to it. As I emphasized in my previous blog and numerous presentations on this topic, “Data is king” – a highly valuable commodity.
    2. Flexibility and customization: Of course, the DIY solution is not a one-size-fits-all approach! Instead, it allows you to customize the solution to fit your exact needs – the parameters to monitor, the specific areas of the plant to focus on the critical systems and the method of monitoring. You choose how to implement the solutions to fit your plant’s budget.
    3. Low long-term costs: As you own the installations, you own the data and you own the equipment; you don’t need to pay rent for the systems implemented through outsourcing.
    4. The specification advantage: As a plant or company, you can add condition monitoring features as specifications for your next generation of machines and equipment, including specific protocols or components. This allows you to collect the required data from the machine or equipment from the get-go.

Disadvantages of insourcing (DIY) condition monitoring:

    1. High upfront cost: Implementing condition monitoring with a data collection system may involve higher upfront costs. This is because there is a need to invest in data storage solutions, engage experts for condition monitoring implementation (typically from an integration house or through self-integration), and employ developers to create or customize dashboards to fit user needs.
    2. Limited scalability: collecting more data requires additional storage and enhanced analytics capabilities, especially when transitioning from condition-based maintenance to predictive analytics. Designing your own solution with limited budgets may hamper the scalability of the overall system.
    3. Infrastructure maintenance: This is another area that requires close attention. Whether the infrastructure is located on-premises, centralized, or in the cloud, the chosen location may require investments in manpower for ongoing maintenance.

Another point to emphasize here is that opting for a DIY solution does not preclude the use of cloud platforms for data management and data storage. The difference between insourcing and outsourcing lies in the implementation of condition monitoring and related analytics – whether it’s carried out and owned by you or by someone else.

Strategic decision-making: beyond cost considerations

The final point is not to make outsourcing decisions solely based on cost. Condition-based monitoring and the future of analytics offer numerous advantages, and nurturing an in-house culture could be a great source of competitive advantage for the organization. You can always start small and progressively expand.

As always, your feedback is welcome.

Edge Gateways To Support Real-Time Condition Monitoring Data

In my previous blog post from early summer, I talked about IO-Link sensors with condition monitoring features that work with PLCs. I covered how condition monitoring variables can be set up as alarms and how simple logic can be set up inside the sensor so it only sets off those alarms to the PLC in real time to alert operators when something is wrong. Many companies, however, take advantage of the IoT sensor data with the long-term goal of analyzing the environmental data conditions to predict maintenance needs in real-time versus relying on a schedule. Some even want to connect directly to their MES systems to inform maintenance personnel of daily maintenance orders, which requires a separate device, such as an IoT edge gateway.

Edge gateway benefits

The biggest benefit of an IoT edge gateway is the ability to process and store large amounts of data quickly, enabling real-time applications to use that data efficiently.

An IoT edge gateway typically sits at the end or edge of your network and gathers all the sensor data either directly from the sensors or from the PLC. Since there will be a large amount of data from all the sensors on the network, part of the edge gateway setup is to filter the relevant and important information and process this vast amount of data. The edge gateway must also handle the amount of data required reliably, and it must have low latency. These important factors are often associated with the gateway’s CPU and memory specifications.

After looking at the performance of the edge gateway, comes the ‘gateway’ aspect which provides a translation to different communications networks, whether local or cloud-based. There are the hardware specs of the gateway, whether it’s using serial, USB or Ethernet for that connection, as well as the environmental ratings on the gateway. Then, more importantly, is the software side of the edge gateway. There are cloud-based communications standards designed for different applications and for either private or public cloud networks.

Edge gateways support different communications protocols, such as HTTPS, MQTT, RESTful API, C/Python API. The gateway portion also helps in the conversion of those protocols and the ease of interoperability to different platforms, such as AWS, Azure, Ignition, and Wonderware. This provides data transparency so that all the data gathered can be used across the many different software platforms.

To get to the IoT end goal, an edge gateway is necessary and it’s important to choose the correct one.

3 Easy Options to Get Started With IIoT in 2022

The Industrial Internet of Things (IIoT) may seem large, intimidating, and challenging to implement; however, new systems and solutions will eliminate the perceived barriers for entry. As we wrap up the year and make plans for 2022, now is a great time to resolve to modernize your facility.

Do you have a process, system or machine that has outlived its life expectancy for many years or even decades and isn’t up to current IIoT standards? Great news: you have several options for updating.

Traditional approach

The traditional approach allows you to use your current controller to output your information to your existing database. If you want to try IIoT on your current setup and your controller cannot be modified, a self-contained system will allow for ultimate flexibility. It will provide you with access to the data based off an extra layer of sensing with a focus on condition monitoring. This approach is the least expensive route, however, if database access is restricted the following options may be better choices.

Cloud-based current industry standard

A second option is to use a portable monitoring system that has a condition monitoring sensor. It is essentially five sensors in one package that can hook up to a system using the cellular network to report data to a secure cloud database. This approach is useful in remote locations or where local network access is limited. If you have a problem area, you can apply this temporarily to collect enough data, enabling you to implement predictive maintenance.

Local-based current industry standard

A local self-contained system is a great solution if a cloud database is not desired or allowed. Systems such as a Condition Monitoring Toolkit allow for recording of devices onto the local memory or USB drive. Additionally, multiple alarm set points can be emailed or extracted locally. This approach is best for testing existing machines to help with predictive maintenance, to improve a process, or even to prevent a failure.

All three of these options require data management and analysis to improve your processor and to remedy problematic areas. Using any of them is an opportunity to test the IIoT waters before fully diving in. Extrapolating the results into problem-solving solutions can allow you to expand IIoT to the rest of your facilities in a cost-effective manner.

Machine Tool Identification with RFID -Automation for Advanced Machining

When most people think of automation in manufacturing the first thing that comes to mind is usually a robot. Without a doubt, robots play an integral part in automating the production process, and let’s face it they are pretty cool. However, there is an often overlooked topic in the automation discussion and that is Automatic Data Collection (ADC), which includes barcode and RFID technology. While it doesn’t carry the “cool factor” quite as well as robotics, RFID has helped automate manufacturing, specifically machining, over the last 30 years.

How is it used?

An RFID tag is placed in the tool holder and stays put for the life of the tool. The tag essentially acts as a mini database that can be read and written to thousands of times.

What type of data is typically written to the tag?

Tool Life, Tool Chain Pocket location, Offset Data, Maintenance Info, etc. Up to 2K of info can be written and read and erased and written again. In addition, this information can be updated on the spot.

What are the benefits of using RFID in Machine Tools?

RFID Improves Quality, Increases Efficiency, and Reduces overall Costs by:

Maximizing Tool and Machine Utilization

  • Precise up-to-date tool life information
  • Accurate transfer of tool offset data
  • Continuous tracking of the tool

Minimizing Human Error

  • Eliminates human data entry
  • Automates transfer of data from presetter to machine
  • Data can be accessed directly on the plant floor as opposed to a database lookup

ToolIDRFID is a tried and true technology that will continue to have a great impact on the machining process. Organizations all over the globe are saving millions every year by utilizing this simple method of collecting and transferring data. Machine tool ID is a no-brainer when quality, efficiency, and productivity matters!

For more information or to learn more visit www.balluff.us.