1. A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection. (May 2021) Authors: Fu, Song; Zhong, Shisheng; Lin, Lin; Zhao, Minghang Journal: Engineering applications of artificial intelligence Issue: Volume 101(2021) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
2. Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction. (May 2016) Authors: Zhao, Minghang; Tang, Baoping; Tan, Qian Journal: Measurement Issue: Volume 86(2016:May.) Page Start: 41 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
3. Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction. (May 2016) Authors: Zhao, Minghang; Tang, Baoping; Tan, Qian Journal: Measurement Issue: Volume 86(2016:May.) Page Start: 41 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
4. CAE‐WANN: A novel anomaly detection method for gas turbines via search space extension. (23rd April 2022) Authors: Zhong, Shisheng; Liu, Dan; Lin, Lin; Zhao, Minghang; Fu, Xuyun; Guo, Feng Journal: Quality and reliability engineering international Issue: Volume 38:Number 6(2022) Page Start: 3116 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
5. Cross-Attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes. (15th August 2022) Authors: Li, Qikang; Tang, Baoping; Deng, Lei; Xiong, Peng; Zhao, Minghang Journal: Measurement Issue: Volume 200(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
6. Deep residual LSTM with domain-invariance for remaining useful life prediction across domains. (December 2021) Authors: Fu, Song; Zhang, Yongjian; Lin, Lin; Zhao, Minghang; Zhong, Shi-sheng Journal: Reliability engineering & system safety Issue: Volume 216(2021) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
7. Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix. (10th July 2015) Authors: Zhao, Minghang; Tang, Baoping; Tan, Qian Journal: Measurement science & technology Issue: Volume 26:Number 8(2015:Aug.) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
8. Gas path parameter prediction of aero-engine based on an autoregressive discrete convolution sum process neural network. (January 2022) Authors: Cui, Zhiquan; Yan, Zhiqi; Zhao, Minghang; Zhong, Shisheng Journal: Chaos, solitons and fractals Issue: Volume 154(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
9. Highly imbalanced fault diagnosis of gas turbines via clustering-based downsampling and deep siamese self-attention network. (October 2022) Authors: Liu, Dan; Zhong, Shisheng; Lin, Lin; Zhao, Minghang; Fu, Xuyun; Liu, Xueyun Journal: Advanced engineering informatics Issue: Volume 54(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
10. Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks. (January 2022) Authors: Zhao, Minghang; Fu, Xuyun; Zhang, Yongjian; Meng, Linghui; Tang, Baoping Journal: Advanced engineering informatics Issue: Volume 51(2022) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗