A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research
Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …
machinery. The majority of these machines comprise rotating components and are called …
Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task
With advanced measurement technologies and signal analytics algorithms developed
rapidly, the past decades have witnessed large amount of successful breakthroughs and …
rapidly, the past decades have witnessed large amount of successful breakthroughs and …
Zero-shot learning for compound fault diagnosis of bearings
Due to the concurrency and coupling of various types of faults, and the number of possible
fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in …
fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in …
A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines
Vold-Kalman order tracking filter is an effective technique for dealing with non-stationary
vibrations which offshore wind turbines often encounter. It has a unique capability to extract …
vibrations which offshore wind turbines often encounter. It has a unique capability to extract …
Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities
Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
Automated and adaptive ridge extraction for rotating machinery fault detection
A ridge in a time-frequency graph (TFG) describes the relationship of a signal component's
instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for …
instantaneous frequencies over time. Accurate ridge extraction from TFGs is beneficial for …
Bearing weak fault feature extraction under time-varying speed conditions based on frequency matching demodulation transform
D Zhao, L Cui, D Liu - IEEE/ASME Transactions on …, 2022 - ieeexplore.ieee.org
Bearing weak fault feature extraction under time-varying speed conditions is a challenging
task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot …
task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot …
A scoping review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion
Rotating machines is an essential part of any manufacturing industry. The sudden
breakdown of such machines due to improper maintenance can also lead to the industries' …
breakdown of such machines due to improper maintenance can also lead to the industries' …
Deep ensemble capsule network for intelligent compound fault diagnosis using multisensory data
With the manufacturing industry stepping into the emerging new era of big data and
intelligence, the amount of data collected from perception and monitoring systems with …
intelligence, the amount of data collected from perception and monitoring systems with …