Automation systems can generate vast amounts of process and plant data, which manufacturing organisations can use to improve operational performance and profitability. Michael Condon, senior product manager, Emerson elaborates how IIoT initiatives help access, manage and analyse this information effectively
Organisations are now looking to build big data analytics capability into new systems and add it to existing operations. Data sourced at the edge can be pre-processed and contextualised before being transmitted to the cloud for deeper analysis, leading to new insights and improved business outcomes.
Manufacturing data was, until recently, mostly attained from PLCs, HMIs, SCADA and historian systems in the operations technology (OT) domain. Because the aim of these systems has been to maximise operational efficiency and uptime by providing control and visibility, their ability to access and analyse data beyond the realm of immediate production goals has been neglected. However, by failing to collect valuable sources of data such as environmental conditions, condition monitoring information and utility consumption, organisations are missing out on significant benefits.
Stranded data exists in these forms:
Data becomes isolated when plant assets have no network access to any OT or IT system. For example, a standalone temperature transmitter with 4-20mA connectivity or Modbus capability would need to connect with an edge device such as a PLC, edge controller or gateway to access this data stream. The data is often not critical to machine control and therefore unavailable via traditional legacy PLC/SCADA data sources. Accessing the data through the nearest machine PLC risks avoiding OEM warranties because of the need for programming logic changes.
Many intelligent edge devices provide basic data ? a smart power monitor, for example, can provide information such as volts, amps, kilowatts and kilowatt-hours. However, extended data sets, such as total harmonic distortion (THD), may not be transmitted due to lack of application requirements, low bandwidth, or limited system data storage capacity.
When a smart device supplies data to supervisory systems via a communication bus, the sampling rate may be too low, the latency too great, or the data set too large for the results to be usable. The data may be summarised before being published, resulting in a loss of fidelity.
Assets sometimes generate data in a format that is inaccessible via traditional industrial systems. Some smart devices have on-board data, such as error logs, which is not communicated via standard communication protocols but would be extremely useful when analysing events causing downtime.
At some facilities, personnel still complete paper forms and documents and do not integrate this data with digital records. In the modern approach, digital methods are used to gather this data, leading to a ?paperless plant?.
Transmitting stranded data to the cloud
Organisations wanting to analyse operational performance across an entire facility, or multiple facilities, are seeking ways to transmit stranded edge data from the field to the cloud, to be historised and analysed. When stranded data is liberated from traditional data sources and transmitted to cloud-hosted applications and services, it creates opportunities to achieve significant benefits. Examples include remote monitoring, predictive diagnostics and root cause analysis, long-term data analytics, like-for-like asset analysis within and across multiple facilities, cross-domain data analysis, and insights into production bottlenecks.
IIoT initiatives can meet the challenges of stranded data and effectively connect edge data to the cloud. These solutions integrate hardware technologies in the field, software running at the edge and in the cloud, and communication protocols, to securely and efficiently transmit data for analysis and other uses. Edge solutions can be an integral part of automation systems or installed in parallel to monitor data not needed by the automation systems. The latter approach enables organisations to obtain the data they need without impacting existing production systems. However, the key is that these new digital capabilities can connect with all forms of stranded data.
Edge connectivity solutions include compact or large PLCs ready to connect with industrial PCs (IPCs) running SCADA or edge software suites; controllers that are ?edge-enabled? and running SCADA or edge software suites; and IPCs running SCADA or edge software suites. Hardware deployed at the edge may need wired I/O and/or industrial communication protocol capabilities to interact with all edge data sources. Once obtained, the data may need to be pre-processed or organised by adding context.
Connecting the edge to the cloud
Hosting software in the cloud reduces costs, as organisations can avoid buying and managing IT infrastructures. Cloud computing enables more computing or data resources to be added in real-time as required. The cloud also eliminates the challenges of configuring, deploying and managing IT hardware and software systems.
Critically, the cloud enables big data sets to be processed efficiently, with CPU processing power scaling based on the needs of the analytics. The data is accessible from wherever and whenever, through any device capable of hosting a web browser. Data security can be enhanced using different servers for storage, with back-up and disaster recovery options.
A cloud architecture is the enabling infrastructure of many IIoT data projects, and the combination of these two technologies enables innovative interactions among people, objects and machines, spawning new business development opportunities.
To download the white paper ?Liberate Stranded Data Via IIoT?, visit here.