Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed

On Seeking the Truth (and being found by it) -- A Christocentric Double Search (Chapter 11 of Befriending Truth: Quaker Perspectives)

The Religious Society of Friends emerged in great synergy with the Seekers of Northwest England three-and-a-half centuries ago, and they still have a great deal to offer the seekers of the 21st Century. In a day and age where more and more young people are registering as none when it comes to their religious affiliation, there nonetheless abides a deep hunger for spiritual reality, which some religious institutions fail to deliver. Because a Quaker spirituality of education envisions the classroom as a meeting for worship in which learning is welcome, the quest for truth is a part of all disciplines, with student and teacher alike seeking to be led into liberating truth by the Present Teacher, who is the Way, the Truth, and the Life On. 14:6). In that sense, it is not only the student and instructor who seek the truth, but each one is also being sought by the Master-as Rufus Jones described it: A Double Search. Seeking the truth and being found by it was the calling of early Friends, and it continues to be the vocation of contemporary Friends whatever tradition they embrace and whatever the context in which they serve

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc