Understanding characteristics, maintenance, and predictive analytics in IIoT

Understanding how IIoT enables acquiring data in real-time in both greenfield and brownfield manufacturing plants is of most importance to factory owners who want to take a few early steps ahead. Unravelling features of amazing predictive analytics and long-term maintenance of a factory is made come true through IIoT. It smartly upgrades everything in a production realm, from machines to product lines to vast enterprises who generate lakes of data every day! The inconveniences, limitations and means of adapting and naturalising into the modern world is now mandatory to grasp the attention of the market. Since IIoT delivers magnified predictive technologies, and open doors for long-term digital evaluation, data acquisition, accumulation, analysis and control are all set in one virtual kit.

Read this article to learn how these technologies can transform prevailing or new industrial bodies to smart businesses that don’t struggle with data handling.

What is IoT and IIoT?

Internet of Things ‘IoT’ is one of the revolutionary tools used in Industry 4.0 that transform something to something smarter. Homes to smart homes, watches to smart watches, warehouses to smart warehouses and more. It is the integration of all things within ecosystems via the internet. IoT allows data transfers between internet-allowing devices with, without or with partial human intervention.

Businesses achieve a lot through interconnectivity. Industrial IoT or ‘IIoT’ unlocks cooperative virtual environments for all people to work, communicate, share and profit under one digital umbrella. It is the IoT of the business world. Large scale companies and industrial systems take sensitive decisions based on sophisticated digitised tools enabled by IIoT. It generates useful data that is mandatory for managing a sustainable business, as the world is gradually disposing old-fashioned data handling mediums. It is also noteworthy that unlike IoT, IIoT is now not a technology that you ‘wish to have’, it is a ‘must-have’ technological base to support your businesses.      

IIoT and data

As IIoT allows numerous elements to be interconnected at every point, data pools around at every end of industrial networks. As a miscellanea of data from various sources will be streaming continuously, useful data will have to be derived, filtered and sorted out from data lakes in order to store in clouds. This massive data pool is known as ‘Big Data’. This digital party gives rise to another problem: How are voluminous sets of data managed? This is where IIoT analytics makes its way to the picture.

Introducing IIoT Analytics

Simply put, it is a collection of analysis tools and procedures that are applied to smartly filter value out from Big Data. What would happen if you were given a yarn of many colourful threads tangled together? You would have to go through a hectic process to detangle it.  First, you sort them, cut off the damaged thread and neatly coil them separately and keep it away to use when you want. The same way IIoT analytics filters, transforms and enriches data- detangle the data streams. Afterwards, the data will be sorted and stored for later use.

The accumulation of data in the analytics software takes quite a journey before it reaches the cloud, but it all happens in split seconds. For example, in a simple motion sensor, data is regularly collected through a smart sensor. This data will be transmitted to a gateway -wirelessly or not-, this roll can be played by an internet-allowing device such as a phone, a tablet or a laptop. Finally, the gateway sends this data to the Analytics cloud to either summarise or pre-process the data as required.

There are two ways to identify how IIoT analysis is applied:

1. With regard to the layer on which it has been applied, which can be:

  • On the premises
  • On the Gateway
  • On the Cloud

2.  With regard to the complexity of the specific cases

Now let’s focus on more classifications and applications of IIoT analytics.

There are main classes of IIoT analytics that are prevailing in today’s IIoT systems. Keep reading to know how each class is specified with purposes that distinguishes them from one another.

Getting the right information at the right time is always the key for success to companies that use Big Data successfully. But Big data is diverse! Therefore, analytics in IIoT systems falls into three main areas according to the nature of analysis required for certain data types. The content given below elaborates on the classes of IIoT analytics and methodologies used by them.

Understanding characteristics, maintenance, and predictive analytics in IIoT

Classes of analytics

Descriptive analytics – what has already happened?

This time mainly focuses on what the business is already dealing with. The following example will help you understand this category better. Some examples for descriptive analytics are given below:

  • KPI monitoring
    Strategies used by businesses to evaluate on main points of performance behaviours for example, hospitals can use it to assess patient stays, identifying rush hours at clinics, keep track of frequents clients, understand patient behaviours at hospital premises etc.

  • Condition Monitoring
    Collecting data in order to describe the condition of the facility or asset which generated the data

  • Anomaly Analytics
    Analysing abnormal behaviours of data in real-time. This analytics tool is mainly focusing on detecting frauds, system failures and uncertain events.

  • Diagnostic Analytics
    A complex analysis strategy which answers a common question ‘Why did it happen?’
Predictive analytics – Predicting future by studying the past!

This class is gaining significance with its feature of making numerical assessments on when or how a possible failure would be made with regard to previous data. This method is ideal in enhancing efficiencies of expensive systems such as airlines, oil and gas plants and other healthcare and transport systems.

  • Prognostic analytics
    This is a predictive analytics method that foresees future events by assessing the rate of anti-climactic behaviours of a system with regard to its predetermined behaviour.
Prescriptive analytics- what aids the business most?

This class uses machine language to assist IIoT systems to decide a post-course of actions based on predictions made by the computer.  It doesn’t predict aftereffects; it relates to actions that can be taken to change those predictions. Two main examples are given below:

  • Condition Based Maintenance and Monitoring Software (CBM)
    This tool analyses the current condition of an asset and generates necessary action to be taken to enhance the sustainability and performance of that asset.

  • Production optimisation analytics
    This method gathers input -algorithms and software data- to model, optimise and analyse parameters statistically to enhance the efficiency of the IIoT systems’ outputs.

  • Analytics in IIoT serves many purposes in many businesses in common such as:
    • Accurately and forecast assess cash inflows and outflows
    • Adopt to data-driven decision-making culture
    • Meet customer product preferences
    • Optimize Distribution and logistic
Understanding characteristics, maintenance, and predictive analytics in IIoT

Methodologies of IIoT analytics technologies

Each class would use different ways to analyse data from the endpoints of the IIoT system before they are systematically filtered, sorted before transferring the data packets to the cloud. Given below are four methodologies used by IIoT analytics.

  • Rule-based
    This method is constrained by predetermined human statements to store, sort or handle data. It mirrors human instructions, therefore behaves like a human mind too. For example, rule-based analytics can be engineered with options after options such as ‘if’, ‘AND’, ‘OR’, ‘only if’ statements etc. For example, IF temperature rises to 80oF, alert the COMPUTER1.

  • Model-based
    Model-based analytics uses classifiers to analyse and predict probable outcomes of a system. It is like using a blueprint to draft similar problems and analyse to scale how successful or not the outcomes are going to be. For example, when the data of a machine’s performance is fed to a model, it analyses the target outcome that can be expected by that machine per day.

  • Physics-based
    This paradigm focuses on generating numerical sampling of real-world physical objects. It virtually recreates natural objects for better visualisation and analysis. It is not a self-destructive method of probing an object, for it allows analysing a product without disassembling it at all.

  • Data-driven
    This approach uses an exclusively data-oriented analysis and interpretation. Reaching goals, maintaining standards and realising statuses are all based on the data analytics generated by the IIoT System.  This approach can be uses data-driven methods to:
    • Enhance the efficiency of business operations
    • Optimise customer experience and meet standards preferred by them
    • Equip a flawless business model

Understanding IIoT analytics these times is of most importance since IIoT is the main technology that is central to predictable maintenance, improved efficiency, energy management and real-time tracking and 100% success of businesses in Industry 4.0. Industrial Innovation comes with change, and there’s no better change that the technological phase brought to us by Industry 4.0.

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