Industry 4.0 considers the combinations of Internet of Things, computing and communication infrastructure, sensors, and artificial intelligence (AI) to provide predictive maintenance and process optimization. These benefits are very relevant to the semiconductor industry, where high reliability and low operating costs are critical for a business’ success. Analyzing the state-of-the-art of the projects that present the implementation of the fourth industrial revolution in semiconductor companies, we noticed mainly two branches of initiatives: (i) articles that explain the final achievements, not detailing how they were assembled and structured; (ii) articles that detail a part of the industry ecosystem, for example, approaching only the communication system or AI algorithms. In this context, this article proposes an I4.0 Pilot as a compilation of lessons learned during an end-to-end development of a reference design applied to a semiconductor packaging and test company. We explore the requirements of clean rooms and information related to sensors and data acquisition boards, in addition to performance details and configurations pertaining to visualization tools and warning notifications. The main contribution appears in presenting the advantages of adopting flexible decisions in the pilot to enable the best characteristics for a final expandable solution. Our final idea is to emphasize the importance of having a pilot project without significant expenses, presenting the reader with the acquired knowledge, and how they can benefit from it.
This paper presents a compression algorithm for power quality (PQ) signals, which does not separate the signal into stationary and transient components. The proposed algorithm is composed of a number of different compression techniques which are combined to improve both compression ratio and total computing time. This method is based on a sequence of three phases. The first phase transforms the signal to the frequency domain using the FFT algorithm. In a second phase the signal is approximated by a polynomial approximation algorithm, which is finally compressed by a lossless compression algorithm. The proposed method is characterized by high compression ratio and low computational cost, making its implementation suitable for embedded systems.
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