Aiming at the difficulty of obtaining fault samples and class imbalanced problem, a modified support vector
data description for fault diagnosis based on both particle swarm optimization and sliding windows(M-SVDD) is proposed
in this paper. The kernel parameters of support vector data description are optimized by the particle swarm optimization.
At the same time, the sliding window technique is introduced. The number of training samples for fault diagnosis model
is controlled by a dynamic adjusted large window. The size of the large window is adjusted dynamically according to the
changes of predicting error of the small window. M-SVDD is applied to the fault diagnosis of copper-converting smelting
process. The experimental results show that M-SVDD can prevent effectively the phenomenon of over-fitting and has good
fault sensitivity and generalization.