Industry 4.0

Condition monitoring

by


Condition monitoring is the process of monitoring a parameter of condition in machinery, in order to identify a significant change which is indicative of a developing fault. It is a major component of predictive maintenance.

Availability of advanced electronics and software in powerful instrumentation, computers and digital signal processors has simplified and extended our ability to control and analyse machinery. Automated systems allow continuous condition monitoring of certain critical items of plants. Significant benefits can be derived from applied monitoring. Measurement and control systems allow early alarming avoids unplanned downtime of machinery and equipment. We can achieve greater plant efficiency by reducing losses due to breakdown and replacement costs.

Critical machines that should have a high availability need to be monitored continually but we need to figure out a way of deciding which parameters should we monitor.
The parameters to be monitored are essentially those that affect operations and maintenance scheduling. Currents and voltages, coolant temperatures, bearing vibration levels, have been measured and will continue to be used. Generally speaking; before some period of time from breakdown, energy consumption of the machine increases. Such patterns can be discovered by machine learning and by further analysis of data but for the sake of the article we are only focusing the first phase, condition monitoring.

Notice that we monitor our equipment ,monitor the condition to preserve and or prevent catastrophic failure. This is great because breakdowns can be frustrating. But condition monitoring doesn’t improve reliability on its own. Improving reliability involves taking a proactive approach and considering root causes of the problem.

All in all,wearing multiple hats and changing perspective will help us make us of condition monitoring excellently. If you like the article and follow up our industrial iot articles you can navigate from Industrial Iot Dictionary

Edge Computing and Accurate Data Collection in Industrial Iot

by

Most people in the industry is talking about big data, deep learning, machine learning, artificial intelligence, data science and digital twin. We need to keep in mind that all of these studies have a single prerequisite, accurate data collection. We can have all the best technology in the world but if we don’t have the data to send to these systems we are not going to get anything.

Data is just numbers without context so we have to do an extra work to transform that data into information. Doing that requires analysis skills and process knowledge. Transformed data should tell us something like, this machine processed that part using these bits and pieces at this time. This is called timestamping. After you’ve done this initial processing you will be able to use it for data science purposes.

There’s a lot also of data in PLCs, robots, sensors and also in our IT systems. First step is connecting these data and putting them into one place, a data lake. Many people say at first step just send all the data up to the cloud but this is not ideal because it will take time to handle that data and it costs.

It is time to give an example for the necessary interval that we log data. Think about a production line with a machine running on a specific rpm and producing a product with A grade and Johny operating the machine at that shift. We have sensors around like temperature and humidity that we read from our conditioning systems.

Is it ok to log all the data at five second interval? How about two or one? The answer depends on the application. If we are trying to find a sudden spike that happens on split seconds one second interval is not enough. Suppose an operator operates a single machine at a shift, it is not necessary to log who is operating the machine in seconds interval.

We need something on the edge of the factory for both OT and IT systems that help us on initial processing of the data. We can use this edge computer to filter and change data into the information throwing away unnecessary information that we won’t need in analytics.

Edge computing pushes data processing to edge devices, meaning workloads are placed closer to the source of data collection. Data transforms into information where action takes place.
Suppose we prepared the data and pushed to the cloud for analytics. Analytics suggests at some point temperature and pressure levels cause valve crash and leads downtime. Analysis of that relational pressure data taking place at cloud servers, the automatic shutoff instructions may come too late. If we leave that job to the edge, relatively higher processing power and very less latency we significantly save downtime, property and even lives.

While emphasizing on the importance of edge devices we do not place less value on computing centers. Although edge devices provide local computing and storage, there will still be a need to connect them to data centers, whether they are on premises or in the cloud. Thanks to high computing power the cloud or on-premise data centers are better when we develop knowledge. When we talk about execution it’s better to be near where the action is happening.

Re-innovate all your Machines and Equipment for Industrial IOT

by

In manufacturing, getting the most out of a capital investment, holding on to older equipment and maintaining it as long as possible is typical. It’s likely to find equipment that is 30 years or older that is still in operation. However, those machines were made long before the internet age. These machines, are often very capable of providing real time information with little touches of experts who have grown up with this older technology and who have experience to bring it forward.

Machines built 30 years ago are typically equipped with either relay logic or outdated Programmable Logic Controller devices. Some of those spare parts are so rare and some hardware failure may lead to unknown stop times. While changing control panel with modern iot ready hardware, new alternative machine equipment should also be considered. At some point modernization of pneumatics or mechanical parts may suit better with the new offerings of the renewed control panel. Some of these early technical methods will require adaptation to enable the sensing of movement, positional location, pressures, and temperatures.

Energy efficiency of a 30 years old machine is not equal to modern ones. Modernization need gives us opportunity to replace old energy consuming parts with modern energy saving parts. The above example is an often case with old motors. Both oversized and undersized motors cause huge spikes in energy consumption. Similarly, other supplementary units such as HVAC systems are also very important on overall efficiency. Reducing the total load required by the machinery is one of our key motivation.

Modernization of these machines saves energy, increase uptime, avoid suffering from spare part extinction. It is highly recommended to spend some time on high tech proof of concepts before final machine deployment. In another article I will discuss specific outcomes of industrial iot.

Using Influxdb as an on premise alternative to SAP Cloud IoT Service – SAP MII

by

In this article i will discuss using Influxdb as an on premise alternative to SAP Cloud IoT Service. Before we move on lets begin with some definitions and facts.

SAP Manufacturing Intelligence Integration (MII) is an application that links SAP ERP with MES. It aims to improve manufacturing operations with real-time analytics. It provides real-time visibility to overall business.

SAP MII is meant to run on SAP HANA which is a high-performance in-memory database that accelerates data-driven, real-time decision-making and actions, and supports all workloads, with the broadest advanced analytics on multi-model data, on premise and in the cloud.

Unlike most shopfloor application technologies, MII works flawless on premise. But there is a missing link for the Sap Cloud Iot service. On premise customers encouraged to use Sap Cloud Iot and thats all. If the on premise users like to integrate their sensor ecosystem they are left alone.

On customer site of influxdata platform i saw SAP used InfluxDB to monitor their Cloud Foundry multi cloud application platform. They chose InfluxDB for its ability to tag metric and event data and enjoy using both Grafana and InfluxDB.

Below comment was so convincing

We researched the best time series platforms and chose InfluxData because it is highly performant, provided high availability, and was more cost effective than the SaaS solutions we were considering,” said Christina Abraham, VP, Global Cloud Infrastructure & Operations at SAP Ariba. “InfluxData was easy to implement and is now the primary data store to collect metrics and events for SAP Ariba operations. InfluxData provides the basis for all the visualizations and alerting that is critical to our business.

I have investigated options to integrate mii with influxdb, so that i could use an on premise timeseries in memory database to store sensor driven bigdata.

In this part of the article i will try to explain the technical steps i followed. I assume you have a working influxdb server. If you dont you should set it up before moving on the below steps.

First you will need to open up your mii workbench and create a transaction.

Create an http post by drag and drop. Then assign the following properties to http post.


HTTP_Post_0.URL="http://yourserveripaddress:8086/write?db=yourdatabasename"


HTTP_Post_0.PostData="temperature,device=Machine1 value=29"

If you run the transction you will see the static string line query is processes on your influxdb.

The above postdata is an example insertion using influxdb line protocol. If you want you can use Local.Variablename Repeater_0.Item etc.  If you have questions leave comments below.

References:

https://www.influxdata.com/customer/sap/

https://docs.influxdata.com/influxdb/v1.8/write_protocols/line_protocol_tutorial/

https://www.sap.com/

Grafana Cloud ,a highly available, fast, fully managed platform

by

In this article i will try to explain you importance of bi tools then share my experience on creating free grafana cloud account.

When we look at the developments of businesses and information technologies in the last 20 years, we can see that one of the most important concepts is data. This importance given to the data also increases the investments such as time and money on its collection and storage. Today, most institutions have already completed the stages of collecting data and storing this collected data.

Business intelligence is the analysis of business data across the entire organization to understand existing business performance and make informed business decisions. The information provided by the business intelligence solution should be targeted and have sufficient level of detail and presentation format for the targeted user group.

Grafana Cloud is a highly available, fast, fully managed OpenSaaS metrics platform operated by Grafana Labs.

Grafana is an open source monitoring tool compatible with most open source software. Grafana is actually a graphical tool rather than a monitoring tool to make the data meaningful. Easy to follow and stylish with a variety of themes and graphics. You can visit the site for detailed information. Do not wake up the worry that we can only watch a machine on the screen after the installation. Create dashboards with machines attached and add those dashboards to the playlist so that the dashboards will rotate at the interval you specify.

I leave you watch the steps to create your free grafana cloud account.

Coronavirus and Industry 4.0

by

Industry 4.0 seems to be receiving a lot of investment by companies because it provides uninterrupted production and healthy communication. I will try to explain how and why.

The corona virus, which appeared in Wuhan, China in December 2019, has spread to every country.The corona virus continues to affect the whole world and make life difficult.

Looking at the state of the industry, it is seen that the manufacturing sectors are the ones most affected by the Covid-19 process. We follow the same situation in agriculture and services sectors. Lack of workforce has caused large, medium or small businesses to close down or shrink in production volume almost anywhere in the world. On the other hand, large companies that use outsourcing and businesses that work as suppliers to them are also affected negatively. Robotics, machine-machine interaction, the Internet of Things and autonomous assembly lines can help to deal with such crises by minimizing human intervention. This is exactly the transformation described as Industry 4.0. In a nutshell, it is the orientation that Covid-19 is expected to accelerate the Industry 4.0 transformation first in mind and then in practice.

Each manufacturer has been affected by this in different ways and continues to be affected. Some situations of manufacturing companies have been badly affected and are looking for different requirements to continue production. Some manufacturers stopped production as demand for their products dropped, while manufacturers producing masks and disinfectants increased their production. So, how will the future of Industry 4.0 be affected by this situation?

Industry 4.0 was an area that attracted many manufacturers before the crisis caused by the corona virus. Manufacturers were considering using Industry 4.0 on issues such as cost reduction, efficiency, sustainability and innovation before the corona virus created. The aim was to make well-run businesses run better. Currently, many manufacturers are using different ways to deal with this situation with as little damage as possible. Industry 4.0 applications are among these solutions.

So what would happen if pre-corona virus Industry 4.0 applications were more common?

The establishment of smart factories with artificial intelligence and robots to maintain, control, evaluate and replan production would enable production to continue without interruption.

Industry 4.0 is a very advantageous technology for situations that bring life to a halt, such as a coronavirus outbreak. It is envisaged that companies will seek solutions to prevent interruptions in production in such situations. Industry 4.0 seems to be receiving a lot of investment by companies because it provides uninterrupted production and healthy communication.