Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis

•A framework based on Bidirectional Gated Neural Networks is proposed for fault diagnosis with uncertainty in dynamic environments.•A Sample Sensitive Bidirectional Gated Neural Networks model is developed to tackle imbalanced fault diagnosis challenges.•Cost sensitive active learning is used to explore unlabeled data.•Effective methods are developed to address both binary Fault Diagnosis and multi-class Fault Diagnosis.