Traditional intelligent fault diagnosis works well when the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, the working conditions can vary between training and testing time. In this paper, we address the issues of intelligent fault diagnosis when the data at training and testing time do not come from the same distribution as a domain adaptation problem using Domain Adaptive Convolutional Neural Networks or DACNN. Our proposed DACNN consists of three parts: a source feature extractor, a target feature extractor, and a label classifier. We adopt a two-stage training process to obtain strong faultdiscriminative and domain-invariant capacity. First, we obtain fault-discriminative features by pre-training the source feature extractor with labeled source training examples to minimize the label classifier error. Then, in the domain adaptive fine-tuning stage, we train the target feature extractor to minimize the squared Maximum Mean Discrepancy (MMD) between the output of the source and target feature extractor, such that the instances sampled from the source and target domains have similar distributions after the mapping. Furthermore, to enable training efficiency in domain adaptation, the layers between the source and target feature extractors in our DACNN are partially untied during the training stage. Experiments on the bearing and gearbox fault data showed that DACNN can achieve high fault diagnosis precision and recall under different working conditions, outperforming other intelligent fault diagnosis methods. We also demonstrate the ability to visualize the learned features and the networks to better understand the reasons behind the remarkable performance of our proposed model.

Bearings, as universal components, have been widely used in the important position of rotating machinery. However, due to the distribution divergence between training data and test data caused by variable working conditions, such as different rotation speeds and load conditions, most of the fault diagnosis models built during the training stage are not applicable for the detection in the test stage. The models dramatically lead to performance degradation for fault classification. In this paper, a novel bearing fault diagnosis method, domain adaptation by using feature transfer learning (DAFTL) under variable working conditions, is proposed to solve this performance degradation issue. The dataset of normal bearings and faulty bearings are obtained via the fast Fourier transformation (FFT) of raw vibration signals, under different motor speeds and load conditions. Then, marginal and conditional distributions are reduced simultaneously between training data and test data by refining pseudo test labels based on maximum mean discrepancy (MMD) and domain invariant clustering (DIC) in a common space. Ultimately, a transferable feature representation for training data and test data is achieved.With the help of the nearest-neighbor (NN) classifier built on the transferable features, bearing faults are identified in this common space. Extensive experimental results show that DAFTL can identify the bearing fault accurately under variable working conditions and outperforms other competitive approaches.

Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy‐dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest‐neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

Condition monitoring and fault diagnosis play an important role in the health management of mechanical equipment. However, the robust performance of data-driven-based methods with unknown fault inputs remains to be further improved. In this paper, a novel approach of condition monitoring and fault diagnosis is proposed for rolling element bearings based on an improved ensemble empirical mode decomposition (IEEMD), which is able to solve the non-intrinsic mode function problem of EEMD. In this method, IEEMD is applied to process the primordial vibration signals collected from rolling element bearings at first. Then the correlation analysis and data fusion technology are introduced to extract statistical features from these decomposition results of IEEMD. Finally, a complete self-zero space model is constructed for the condition monitoring and fault diagnosis of rolling element bearings. Experiments are implemented on a mechanical fault simulator to demonstrate the reliability and effectiveness of the proposed method. The experimental results show that the proposed method can not only diagnose known faults but also monitor unknown faults with strong robust performance.