Machine learning managed services can big tech provide viable alternatives to iiot predictive maintenance software – insidebigdata 9gag memes

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At a high level, one can argue that the introduction of new AI tools is a positive development because they provide more options in the marketplace. However, the release of generic machine learning developer tools does not address the underlying needs for IIoT predictive maintenance, nor can it counter the lack of available big data scientists.

Let’s start with “build.” Building an in-house machine learning predictive maintenance solution requires two competencies: (1) experience with software development using the Software Development Lifecycle (SDLC) and (2) deep machine learning expertise.

The following chart depicts the process for building machine learning-based software. It is important to keep in mind that IIoT predictive maintenance software is different from generic “predictive” software. The contextualization of the solution is a critical component defining the design’s functional and technical specifications. For instance, does the solution address predictions of downtime for stone-crushers or electric generators? Is it suitable for renewable energy or a fossil fuel power plant?

Amazon and Google provide generic machine learning tools that are not customized to the needs of the vertical segment, workload or machinery type. Even qualified machine learning experts must select an algorithm from an machine learning library for the data that is analyzed. It is unrealistic to expect an unqualified technician to make decisions about which machine learning algorithm to select for a given data set. Furthermore, in the case of predictive maintenance, the skill-set requirements are even more complex: The industrial plant needs professionals with expertise in both the specific machinery and machine learning skills.

Open Source Software (OSS) is defined as “a type of computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone and for any purpose.” Open source tools are actively used by highly trained machine learning / AI professionals.

The machine learning and AI community already relies on open source. Python is an open software development language that is commonly used. Both TensorFlow (an open source deep learning framework) and Keras (an open source neural networks wrapper library) are written in Python.

Unscheduled asset downtime costs the global process industry $50 billion a year. Increasing up-time offers industrial plants a significant financial opportunity. Amateurs are likely to make mistakes and waste time at the expense of their other responsibilities. Plant responsibilities placed in amateurs’ hands using machine learning platforms can result in significant revenue loss, failure and physical damages.

Development of the core asset and intellectual property for IIoT cannot be left in the hands of unskilled employees who are likely to waste time on tedious and time-consuming trial and error. More importantly, without understanding the underlying process for developing a model, the inexperienced developer does not realize that a few months of operational deployment are necessary to determine whether the best model was selected. Trial and error leaves the industrial plant vulnerable to unforeseen consequences.

The potential impact to the business should not be overlooked. Missing evolving asset degradation and the threat of machinery breakdown risks production yield rates and revenue. The domino effect of poor maintenance standards impacts many aspects of industrial operations, including the health and safety of employees.

IIoT predictive analytics applies advanced AI algorithms to the data generated by machine sensors. This is used to predict upcoming equipment failure. The goal is to reduce unscheduled asset downtime and increase production yield rates and revenue. Generic AI tools are not designed to perform specialized tasks such as identifying evolving asset degradation and providing a Root Cause Analysis.

There is also a third alternative for industrial plants to consider. There is a new class of commercial solution s that appl y advanced AI algorithms to sensor data. Plant technicians receive alerts about upcoming failures and can dig in and remediate accordingly. Operations and Maintenance continues playing its vital role within the industrial plant without requiring that maintenance and reliability professionals gain expertise in big data or machine learning software platforms.

Ad-hoc open source tools that do not scale or address the specific predictive maintenance requirements of an industrial plant cannot be used to bypass the big data skill-set gap in the job market. Adding more tools and algorithms to the mix does not help unless the people tasked with AI have the necessary expertise.

Deddy Lavid is CTO of Presenso. He is a senior algorithm and software developer and expert in the field of machine learning and big data architectures. Coming from RAFAEL, where he led a team of algorithm developers in large SW projects of some of national importance. Deddy holds Honors M.Sc. computer and information science.