Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.

Novel heteroaryl triflones including oxindole, pyrazolone, pyridine, and quinoline derivatives have been regioselectively synthesized by LDA-mediated thia-Fries rearrangement for the first time. These reactions are also the first examples of the application of anionic thia-Fries rearrangement in heteroaromatic compounds.

A convenient synthesis of indole triflones is reported. N-Alkyl, aryl and N-H indole triflones were obtained in 82-96% yields by the Tf(2)O/TTBP System. Biindolyl triflones were accessed in 51-81% yields for the first time by simple treatment of the resulting indole triflones with a base and without any use of organometallic chemistry. An environmentally friendly solvent, Solkane 365/227, can be substituted for this process without any loss of efficiency.

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