The 9 Artificial Intelligence trends 2020 to keep an eye on – pt1

Artificial Intelligence, protagonist of the scientific debate in the recent years, is the technology that is revolutionizing our industry, as electricity did more than 100 years ago.

While in the field of services, finance and medicine, AI techniques have already been applied for years, in the industrial sector they have recently come to support operators in O&M activities, process control and energy efficiency.

The growing presence of a solid database, which is the result of the IoT investments made in the past years, and of new technical standards defining how and what to monitor – combined with increased market awareness – open the way to new solutions and applications.

As in all fields, it is very easy to make long-term predictions about the spread of artificial intelligence. How close is the future world where machines will be autonomous and thanks to artificial intelligence the human supervision will not be necessary any longer?

Rather, what is already in the final stage of experimentation or is already available and can be applied by Italian and international companies with a sufficient digital maturity? (To evaluate the digital maturity of your company, you can fill in the simple assessment questionnaire that we have created covering different business areas: request it here).

Below, we look together at 9 Artificial Intelligence applications that are already reality in 2020.

1. Predictive and on-condition maintenance for processes, assets and plants

Predictive maintenance through artificial intelligence is the main focus of AI applications in manufacturing. Many international big players are investing on AI and predictive maintenance in order to digitalize processes and support the O&M teams in the management of failures and anomalies. Thanks to the increasing amount of data and the efforts to standardize databases, these technologies become more and more accessible.

The fundamental limit encountered so far is the comprehensibility of the results and the models industrialization. These two problems can be solved through the use of intuitive user interfaces (which require to properly study the process and interpret the results), and the installation of the solutions in cloud. This way, AI-based predictive maintenance solutions will be increasingly accessible to teams that operatively need information, without the need to understand control charts.

(We have already written some case studies on this topic: you can find them here)

Digital maturity required: integrated and interoperable
  • – Models must receive data at intervals of not more than 1 hour
  • – It is necessary to have a historicized database adequate for the models training
  • – A hardware and software architecture is also needed to support the communication flow and installation of the AI platform (you can look at our solution for industrial IoT here).

2. Image recognition for quality control

Whether it is manufacturing of precision mechanical parts, seals, or bottle caps, all process lines have to ensure a low percentage of scraps. Above all, they need to be integrated in order to automate the recognition of parts under defined quality standard.

Often, screening techniques alone are not enough to find a small scratch or a wrong element. This is why some companies are evolving their quality control algorithms to include intelligent models that can learn by  themselves which pieces are good and which have to be discarded.

Thanks to machine learning, quality control mechanisms increase their accuracy, ensuring product quality with less margin of error.

Digital maturity required: oriented to the digitalization – 
  • – It is necessary to have a good number of historicized pictures of good and defective parts. With the correct information the intelligences can learn to recognize the features that make a piece flawed
  • – It is necessary to have or install a controller that can support machine learning on edge and coordinate the procedure to report the defect consequently.

3. Anomalies detection through image recognition

Predictive maintenance is often not easily applicable in many and various causes: from the difficulty of installing sensors in some areas to the data property issues (as they have those companies that provide only maintenance services), up to the difficult representation of the physical phenomena behind the fault.

For some cases, however, it is possible to apply images recognition algorithms to identify assets and plants problems. It can be through a photo or an image from a thermal camera.

A maintenance specialist can understand if there is a problem just by looking at certain types of assets (filters, pipes, pictures of panels, etc.). By training intelligences on the basis of expert technicians know-how, it is possible to implement a model capable of doing the same.

With the same technology with which Facebook recognizes the faces of your contacts or advanced clinical diagnosis are made, it is possible to identify visible anomalies in the assets and automatically send a request for a detailed audit, also by new maintainers who do not have the same experience.

Digital maturity required: managed –
  • – It is necessary to have a trained technician to engage in the initial part of training and a database of asset photographs
  • – With a good system of asset management and CMMS it is possible to structure a database sufficient for the purpose

The solution can be connected directly to the CMMS, and its use does not require a continuous dataflow, hence the implementation of AI for the identification of visible anomalies is relatively simple.

4. Anomaly detection and data quality

The base of artificial intelligence is the data, quality data, that has to be controlled. It is however typical of field data to be incomplete, blocked or abnormal. Thanks to artificial intelligence, it is now possible to spot and process strange values.

Standard cleaning procedures risk not to detect some anomalies while intercepting others that are not real anomalies. Artificial Intelligence on the other hand can understand what is the correct trend of data from the meter under control. Solutions of this type, installed on edge or in cloud, guarantee a data truthfulness that exclude problems of peaks, freeze or misalignment of the measurement compared to the normal values.

 Digital maturity required: initial –
  • The intelligence is autonomously able to learn and it could be virtually installed also on the home counter. Clearly, an organization that requires a technology like this has reached a fairly high digital maturity, which leads it to want to ensure data quality quickly and reliably.


These are the first 4 trends of artificial intelligence in 2020, on Monday we will publish the following 5 to understand how to put AI at the center of industrial processes.

Digital maturity levels:

  • Level 1: Initial
  • Level 2: Managed
  • Level 3: Defined
  • Level 4: Integrated and interoperable
  • Level 5: Digital Oriented

Other Topics:

We organize AT LEAST one webinar per month on the topics of maintenance engineering, energy efficiency and the latest artificial intelligence trends.

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