Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Ming J"'
Publikováno v:
Mechanical Systems and Signal Processing. 168:108657
Deep learning methods have shown great potential to provide reliable remaining useful life (RUL) predictions in Prognostics and Health Management applications. However, deep learning models, particularly supervised learning methods, are strongly depe
Autor:
Yuejian Chen, Ming J. Zuo
Publikováno v:
Mechanical Systems and Signal Processing. 167:108539
Gearboxes often operate under variable speed condition which makes the collected vibration signal, a widely employed type of condition monitoring data, becomes non-stationary. This paper proposes a sparse linear parameter varying vector auto-regressi
Publikováno v:
Mechanical Systems and Signal Processing. 167:108524
This paper proposes a new feature extraction method using time–frequency image data for fault diagnosis of variable-speed rotating machinery. Time-frequency representation (TFR) is widely used to analyze time-varying behaviors of rotating machinery
Publikováno v:
Mechanical Systems and Signal Processing. 109:166-184
This paper presents a signal processing scheme, namely enhanced morphology gradient product filter (EMGPF), for rolling element bearing fault detection. In this scheme, a morphology gradient product operation (MGPO) is firstly proposed to extract imp
Publikováno v:
Mechanical Systems and Signal Processing. 165:108327
Diagnosis of gearbox tooth cracks at an early stage is important to prevent catastrophic failures. The time synchronous average (TSA) of vibration signals of gearboxes with a tooth crack mainly consists of the gear meshing frequency (GMF) and its har
Publikováno v:
Mechanical Systems and Signal Processing. 158:107744
Intelligent fault diagnosis of machines has received much attention in this big data era. Most reported models are constructed under the assumption that the training and testing data are from the same distribution. However, data distribution will shi
Publikováno v:
Mechanical Systems and Signal Processing. 95:468-487
Local mean decomposition (LMD) is a promising approach to implement time-frequency representation (TFR) for multicomponent amplitude-modulated (AM) and frequency-modulated (FM) signal analysis; however, its performance usually suffers from end effect
Publikováno v:
Mechanical Systems and Signal Processing. 156:107605
In industry (e.g., wind power), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults and assess their severity using vibration signals collected under different speed profiles. A few studie
Publikováno v:
Mechanical Systems and Signal Processing. 155:107623
Vibration signals related to planetary gearbox faults under non-stationary conditions will be equipped with sinusoidal modulation laws in their amplitude modulation (AM) and frequency modulation (FM) modes. Extracting fault features implied in the AM
Publikováno v:
Mechanical Systems and Signal Processing. 134:106342
Time series model-based approach (TSMBA) is promising in processing vibration signals and assessing the health condition of gearboxes. Accurate time series modeling of the baseline vibration is critical to the TSMBA. Gearboxes often operate under tim