|Accessible Engineering Innovation
|10 June 2020
|DDS in Smart Manufacturing
Here to cover a large and mixed audience let me touch on a few fundamentals quickly. Figure 2 presents the inforation in terms of the Automation Pyramid.1). Here is a picture of how industry automation works and the different levels of automation. Any industry starts with the field level and ends with the business logic. Now let us have a quick walk through on every level.
|The Field Level where products are produced. In other words, this is where the physical work plus monitoring occur. Electric motors, hydraulic and pneumatic actuators to move machinery, proximity switches used to detect that movement or certain materials, photoelectric switches that detect similar things will all play a part in the field level.
|The Control Level uses the the control devices to “run” the devices in the Field Level. The Control Devices make decisions based on information provided by sensors, switches, and other input devices to complete the programmed task.
|The Supervisory Control and Data Acquisition (SCADA) is combines the Field and Control Levels to provide oversight from a single location. This is usually accomplished using Graphical User Interface, or Human-machine interface (HMI), to remotely control operations. For example, water plants often employ this technology to control remote water pumps.
|The Planning Level uses Manufacturing Execution System (MES) to monitor the entire manufacturing process. For example, in a factory to plan for everything from raw materials to the finished products. This allows management to visualize the current state of operations and aids them in making decisions and adjust raw material orders or shipment plans based on real data received from Supervisory, Control and Field Levels.
|The management level uses the companies integrated management system such as as Enterprise Resource Planning (ERP). Corporate management visualize and control operations. This level allows the businesses monitor all levels (i.e., manufacturing, to sales, to purchasing, to finance and payroll). The integration of an ERP promotes efficiency and transparency within a company by helping to communicate the levels.
To begin with, the field level is the production flow that does the physical work and monitoring and at the Control the information from all then sensors is collected level and is used to make decision The supervisory layer that is the human layer or SCADA the information that is used to access the data to control the systems from one single location and plus it usually adds some graphical user interface for plant managers aspect. The Planning layer (Manufacturing Execution System (MES)) it monitors the entire manufacturing process in a plant or a factory The business logistics (Cloud) layer - it monitors or levels of from manufacturing to sales to purchase and to finance
So in this entire automation pyramid the complexity of managing the sensor nodes including the data transfer, control, configuration, and maintenance is the biggest challenge because this is where the data originates and every system reacts and responds based on the sensor data in every level of automation.
Also in comparison to the other automation levels, the field level has the highest market share aspect
So this is the populations nodes per industry and they are limited to.
Now you can imagine complexity of managing this bigger population of sensor nodes in terms of configuration, control and maintenance.
These are the problems and use cases on managing the sensor nodes.
The use cases can range from
For this variety of use case scenarios, the problem statement, the major challenges in a Sensor Network are
The strengths of the Data Distribution Service (DDS) standards and how they differ from other communication standards. DDS is an interoperable, platform independent, secure, scalable communication standard. It is our opinion that DDS is the right choice for these large scalable networks.
The following discussion is on the DDS features and concepts and how they allow to they align to these challenges.
The Predictive Maintenance is a strategy using analytical and statistical techniques on past data to perform extrapolations about the future. The strategy is referred to as Predictive Analytics. The analytical methods are applied to maintenance issues fo predicting potential failures. The methods use patterns and anomalies, but is usually deployed when probability of imminent failure is high. This helps in allocating limited resources, maximizing device or equipment uptime, enhancing quality and supply chain processes. The end goal is to improve the overall satisfaction for all the stakeholders involved.
In Figure 6, the flow of data from sensors to Analytics is represented.
Within the chemical industry there is a need to mix chemicals in a controlled environment to assure the safety of the process and to avoid waste of materials and time (in other words, the efficiency of the process).
The overall process is to mix various and often expensive chemicals in multiple containers using an agitator. The agitators are moved in a prescribed rate of speed to ensure that the chemicals are thoroughly mixed and at a rate that allows the chemical reactions to work as intended. When a chemical process is initiated and a fault occurs, the initial chemical resources are wasted. Failures in this process are costly in terms of raw resources, loss of end-product and time. Potentially, in some processes the containers, the agitators, sensors themselves might need to be replaced. Therefore, any changes in the state of the chemical rate and speed of the chemical mixing might result in damage to the container, the agitators or drivetrain might idle the plant for days, weeks or months greatly damaging revenues. To mitigate this, the condition of the motor and the stirrer are monitored continuously in real-time. The data generated is used within the predictive models applied to all the automation layers to identify potential failures and apply the correct maintenance or repairs before the start of a new production. The data granularity, integrity, and the prevention of dataloss are essential for the predictive models to accurately predict failures and to recommend maintenance or replacement of parts.
As a result of all these factors, the Container Agitator in Chemical Industry is a good use case for DDS in helping maintenance operations.
The Challenge is to ensure the reliability of the chemical mixing process by reducing the risk of failure of components (i.e., containers, agitators, motors and sensors) reused within the chemical mixing process . The components need to maintained or replaced, when needed, in a timely way without jeopardizing the overall efficiency of the endeavor or adding unnecessary replacement overhead.
A problem confronting cities as they try to move and adapt to the Smart City Sensor driven concepts and initiatives is how to scale the Sensor Network to not just include more facilities but how to expand it to cover more city services within the sensor network ecosystem. For example, a city might need to expand its sensor network to add 1,000 homes a year, but they also want to add sensors for water, sewage, lighting, Electric Grid, natural gas, and communication grids (i.e., cable television, Phone and internet). In addition to these “traditional” city services, there is a desire to add traffic monitoring, emergency services, transit, parking, recreational facility usage and security services to the ecosystem.
As a result of all these factors, the Smart City scaling is good use case for Data Distribution Service (DDS) in helping the continuous scaling needs spanning many kinds of sensors and spanning years operation which also highlights the needs for interoperability.
Data Distribution Service (DDS) addresses the following needs:
These are the features of DDS that are applicable in addressing the needs of the Chemical Mixing and the Smart City Use Cases previously discussed.
The DDS Enterprise Level Solution Architecture is depicted in Figure 10 is applicable to both the Chemical Manufacturing and the Smart City uses case already discussed. The architecture is mapped against the Five Layers of the Automation Pyramid (See Figure 2).
|The Field Layer of the architecture with highly network connected sensors forming a sensor network that can publish information about the domain specific equipment or can subscribe to control topics generating instructions generated by the Control Layer.
|The Control layer has Data-Centric Publish-Subscribe (DCPS) Entities that receive information from the Field Layer and create data readers and writers on topics that are specific to the particular domain (i.e., Electric Grid, Manufacturing, Oil and Gas, etc.).
|The Supervisory Layer uses the information that is published on by the Supervisory Layer as a databus. It reads information from the databus, or processes it and can either issues messages back to the Control Layer or can abstract the information and publish to the MES using DDS Web Services.
|The MES Layer (sometimes referred to as Fog) reviews the data provided by the Supervisory Layer and looks applies Classification Rules, Pattern detection, specifies actions and processes Predictive Analysis
|The Cloud Layer represents Plant Managers, owners and customers that are interested in results of the manufacturing operations.
This is the solution architecture that has been proposed for these kind of complex use cases.
In the future, DDS will be the natural choice for the large scale network system in the real world.