Outcomes , Insights and Best Practices from IIC Testbeds : MQM Testbed
Huawei , which enables a system to pick up the field signals and train the system , based on the collected data and previous results . This leads to correct decision-making based upon the preset policies and rules . This analytic engine was originally built for another project . The team was lucky to have it modified for the MQM Testbed . The only criteria communicated to the AI – based analytic engine development team was that the analytic engine must make the right call with the focus on avoiding faulty detection by passing a product that is actually defected . Fortunately , the analytic engine was easily modified to suit this purpose . If not , additional delay may have occurred if the testbed team needs to start over .
AI-BASED ANALYTIC ENGINE
Up to this point , people may question using an AI-based analytic engine for this acoustic detection task as overkill . The process may take place with a dedicated machine and does not need to involve many computations . An AI-based analytic engine , such as the one provided , performs machine learning and deep learning , which requires not only the computing power of an ARM processor 2 , but also some other processors like Graphics Processing Unit ( GPU ) and CPU to support it . This was a conscious decision by the testbed team because it could help yield an additional benefit for the MQM Testbed : with the AI-based analytic engine , the MQM Testbed was not only very useful for the simple acoustic detection task , it could also be used for other purposes in the future .
It is important to note that the training data sets for the AI-based analytic engine may not be easily acquired . The key to machine learning is the training data sets . Project partners provided field recorded data from machines in good working order ( passing data ) and the machines being returned ( failing data ) after shipping . Colleting the failure data sets was not an easy task because the quantity of the failed machines is relatively small for the short data collecting period . As a result , the data set size of the failing data is much smaller compared to the size of the data set for passing data . All data sets are fed into the learning process of the machine to train the machine to identify the difference between passed and failed states . The machine eventually established a passing state and failed states through testing , training and fine-tuning . This analytical engine development is very important as the AIanalytic engine is not something available off-the-shelf . For users who plan to modernize an existing plant , the complexity of the project cannot be underestimated .
TESTBED COLLABORATION
One challenge of promoting the MQM Testbed is that colleagues from Haier have been unable to travel to IIC meetings to share their knowledge with members . The testbed spokesperson has communicated MQM Testbed conversations to IIC members with the goal of developing this testbed further within IIC . While other IIC members may have similar analytic engines , there are aspects that are unique to Huawei ’ s approach for the Testbed as IIC members are able to duplicate the process and develop
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An ARM processor is one of a family of CPUs based on the RISC ( reduced instruction set computer ) architecture developed by Advanced RISC Machines ( ARM ). TechTarget , How to choose the best hardware for virtualization .
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