… In the recent years, the amount of data collected has grown exponentially … machinery fault
detection systems [2]. This development means that fault diagnosis enters the era of big data …

… Based on the characteristics of 1-D mechanical vibration signal, this paper proposes
Supervised Data Augmentation (SDA) as a regularization method to provide more effective training …

… to perform machinery diagnosis using machine-learning comprises data acquisition, signal
… used for pattern recognition in machinery fault diagnosis. Several classification algorithms …

… a data augmentation algorithm based on the core assumption of Order Tracking and present
a self-adaptive convolutional neural network for fault diagnosis… of industry, machinery health …

… Besides, traditional machine learning methods have also been applied for rotating machinery
fault diagnosis, and some typical models developed with different modification strategies …

… conditions of the machinery are constant. It is inevitable that the equipment runs under
large speed fluctuation in real industries. To achieve data augmentation under the condition of …

… an intelligent diagnosis system is challenging. This paper proposes a fault diagnosis method
… and deterioration of a bearing which can escalate and lead to severe equipment damage. …

… from augmented synthetic data to real rotating machinery is here proposed, namely FaultD-XAI
(Fault Diagnosis … synthetic data with data augmentation to avoid the need of real fault …

… assessable data augmentation named ADA is proposed for mechanical fault diagnosis …
In modern industries, rotating machineries such as bearing and gear are applied widely. For …

… A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions
… An early fault detection method of rotating machines based on unsupervised sequence …