Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

Rolling bearings play a vital role in the overall operation of rotating machinery. In practice, many learning methods for variable-speed fault diagnosis ignore task-specific decision boundaries, making it very difficult to completely match feature distribution between different domains. Therefore, to overcome this problem, an adversarial domain adaptation of asymmetric mapping with CORAL alignment is presented. The asymmetric mapping feature extractor is able to extract more specific-domain features with obvious distinction. Meanwhile, combining the maximum classifier discrepancy of deep transfer to give an adversarial approach and taking the task-specific decision boundaries into account, class-level alignment between the features of the source domain and target domain can be attempted. To prevent degenerate learning, which is possibly caused by asymmetric mapping and adversarial learning, the model is constrained by deep CORAL alignment to extract more domain-invariant features. Experimental results show that the proposed method can solve the variable-speed (a small span of intermediate vehicle speeds) fault diagnosis problem well, with high transfer accuracy and strong generalization.

In recent years, transfer learning has become more and more favored by scholars from all walks of life. At present, although transfer learning has achieved certain results in the field of fault diagnosis, the use of transfer learning alone may lead to poor transfer effects or even negative transfer due to the sample gap being under variable conditions in the same machinery. Therefore, deep domain adaptation with adversarial idea and coral alignment (DAACA) is proposed in this paper in order to solve the problem. DAACA is briefly summarized below. The domain adaptation with adversarial idea is added on the basis of transfer learning. The deep coral is then appended to further reduce the distribution difference between the data from the source and the target domain, which improves the invariant features of adversarial domain adaptation learning. In addition, a gradient reversal layer is introduced in the method to achieve gradient reversion and avoid the adversarial disadvantage of fixing parameters separately. It can be seen from the experimental results that the DAACA can not only solve the problem caused by the sample gap in variable conditions, but also achieve higher diagnosis accuracy and generalization ability.

As mechanical fault diagnosis enters the era of big data, the traditional fault diagnosis methods under variable working condition are difficult to be applied because of the massive computation cost and excessive reliance on human labor. For the application of intelligent fault diagnosis under variable working conditions, the crucial difficulty is that the variable speed or load can cause smearing and skewing of classable feature. It is the key to break the predicaments by extracting the features which are irrelevant to the working conditions and contain fault information. This paper propose a new intelligent fault diagnosis framework under variable working conditions called Data-driven Fault feature Separation Method (DFSM) which can eliminate the working condition features from all the information and employ the rest fault information for diagnosis. In our DFSM, classification loss ensures the basic classified ability, first. Second, uncorrelation loss increases the discrepancy between fault features and working condition features. Then, reference loss guides the working condition encoder only extracting the working condition features. Finally, autoencoder loss ensures that all the information has been extracted. It should be noticed that our DFSM is trained only using the dataset under a certain working condition and can diagnose faults with high accuracy under variable even time-varying working condition, which means that it is easy to be applied. The experimental results of rolling bearing dataset show that the proposed DFSM can not only break the limitation of existing methods, but also achieve a superior performance comparing with related method. Besides, through the visual understanding, the proposed DFSM is certified that it is able to eliminate the working condition information and extract precise classable feature for fault diagnosis.