Predictive Operations and Industrial AI: Top Trends to Watch in 2020

Industrial AI Image Credit: nist.gov
Industrial AI
Image Credit: nist.gov

With businesses digital transformation move, AI is gaining rapid hype in industrial settings.

The last few years have seen tremendous development in AI and machine learning. From the introduction of new applications, such as advanced analytics, next-generation automation platforms, natural language and processing support, and others, to the increased exploitation, we can see a great tide of AI everywhere. Enterprises, in various industrial settings, are using AI-driven systems to automate and reinvent fundamental industrial processes. In this scenario, it is commonly called industrial AI. This artificial intelligence application automates the movement of materials, enables predictive maintenance, handles complex supply chains, along with optimizing design validation processes.

As AI is continuing to disrupt complex sectors, including manufacturing, metal, mining, oil, and gas, the global industrial AI market is forecast to reach US$72.5 billion by 2025, from US$15 billion today. 

Here is a look at top trends that will drive the growth of industrial AI in 2020 and beyond.

 

Predictive Maintenance

Manufacturers nowadays seek predictive maintenance to advance to Industry 4.0, or the fourth industrial revolution. Most predictive maintenance systems rely on machine learning techniques to formulate predictions. These systems use algorithms to foresee the next failure of a component, machine, or system and then alerts authority person to perform focused maintenance procedures to dodge the failure, but not too early so as to waste downtime unnecessarily.

 

Data Access

Keeping data agile, accessible and high quality can be a challenging job for companies, as it requires knowledge and skills in data visualization, data integration, data management and monitoring, data cleaning and validation, and more. In this regard, industrial AI has a role to play accessing and analyzing the data quickly and holistically. It effectively performs tasks, such as data acquisition, data filtering, data cleaning and integration, all in real-time.

 

Alliance Between Tech and Industrial Companies

Owing to the lack of in-house expertise, more industrial companies seek out IT support from technology players. For instance, around 40 major oil and gas companies were using Microsoft Azure’s cloud computing service for their AI projects. On the other hand, energy firms are seeking AI to predict potential failures in equipment, corrosion, or security breaches, improve efficiencies and productivity and increase worker safety. Total oil, for example, signed an agreement with Google Cloud to create a system for the analysis of subsurface data that improves the exploration and production processes.

 

Adoption of Turnkey Solutions

Artificial intelligence pervades the entire Industry 4.0 ecosystem across distinct industrial scenarios and is not only limited to the production floor. Though there are several different tools required to go from connectivity to predictive insight, plant operators will only use technological advances that require limited learning effort and that do not present fragmented user experiences. Turnkey solutions are ready-to-go, full package solutions that have both advantages and disadvantages depending on the business and its objectives.

 

Generative Design

Industrial AI can be used in the design phase. It allows designers and engineers to capitalize on AI algorithms, generally referred to as generative design software, to explore all the possible configurations of a solution. It helps turn tedious engineering design processes into a sophisticated yet natural interaction between computer and engineer. The most basic form of generative design is topology optimization. This process algorithmically evaluates a user’s design and classifies areas where mass can be removed without compromising the design’s integrity.