Precision Dairy Edge, Albeit Analytics Driven: A Framework to Incorporate Prognostics and Auto Correction Capabilities f
Oxford English Dictionary defines Prognostics as “an advance indication of a future event, an omen”. Generally, it is confined to fortune or future foretellers, more have subjective or intuition driven. Data Science, on the other hand, embryonically enabl
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Hanumayamma Innovations and Technologies Private Limited, HIG – II, Block – 2/Flat – 7, Baghlingumpally, Hyderabad 500 044, India {skedari,jaya.vuppalapati}@hanuinnotech.com 2 Hanumayamma Innovations and Technologies, Inc., 628 Crescent Terrace, Fremont, CA, USA {ailapakurti,cvuppalapati, Sharat,raja}@hanuinnotech.com Abstract. Oxford English Dictionary defines Prognostics as “an advance indication of a future event, an omen”. Generally, it is confined to fortune or future foretellers, more have subjective or intuition driven. Data Science, on the other hand, embryonically enables to model and predict the health condition of a system and/or its components, based upon current and historical system generated data or status. The chief goal of prognostics is precise estimation of Remaining Useful Life (RUL) of equipment or device. Through our research and through industrial field deployment of our Dairy IoT Sensors, we emphatically conclude that Prognostics is a vital marker in the lifecycle of a device that can be deduced as inflection point to trigger auto-corrective, albeit edge analytics driven, in Dairy IoT Sensors so that the desired ship setting functions can be achieved with precision. Having auto-corrective capability, importantly, plays pivotal role in achieving satisfaction of Dairy farmers and reducing the cost of maintaining the Dairy sensors to the manufacturers as these sensors are deployed in geographically different regions with intermittent or network connectivity. Through this paper, we propose an inventive, albeit, small footprint, ML (Machine Learning) dairy edge that incorporates supervised and unsupervised models to detect prognostics conditions so as to infuse autocorrective behavior to improve the precision of dairy edge. The paper presents industrial dairy sensor design and deployment as well as its data collection and certain field experimental results. Keywords: Dairy sensors Precision sensors Prognastics Dairy Precision dairy edge Prognosis approach Open system architecture for condition based monitoring OSA-CBM Hanumayamma Innovations and Technologies
© Springer Nature Switzerland AG 2019 K. Arai et al. (Eds.): FICC 2018, AISC 887, pp. 506–521, 2019. https://doi.org/10.1007/978-3-030-03405-4_35
Precision Dairy Edge, Albeit Analytics Driven
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1 Introduction Using system generated data, current and/or historical, one can model and predict the health condition of a system and/or its components. This is the underlying and chief principle of Prognostics [1]. The chief aim of prognostics is precise estimate of Remaining Useful Life (RUL) of equipment or device [2]. Through our research and through industrial field deployment our Dairy IoT Sensors, we emphatically conclude that Prognostics is a vital marker in the lifecycle of a device that can be deduced as inflection point to trigger auto-corrective, albeit edge analytics driven, in Dairy IoT Sensors so that the desired ship setting functions can be achieved with precision. The IoT Edge Analytics is enabler and catalyst for prognost
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