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Domain adaptation technologies have been extensively explored and successfully applied to machine fault diagnosis, aiming to address problems that target data are unlabeled and have a certain distribution bias with source data. Nonetheless, existing fault diagnosis methods mainly explore feature-level alignment strategies to reduce domain discrepancies, which not only fails to directly ascertain the relationship between the target output and domain deviation, but also cannot guarantee accurate diagnosis results (i.e., learning class-discriminative features) when only relying on feature adaptation. In light of these issues, a more intuitive and effective domain adaptation method is developed for intelligent diagnosis of machinery in this article, in which the minimum class confusion and maximum nuclear norm-based target prediction constraints are simultaneously designed to promote learning reliable domain-invariant and discriminative features for accurate fault diagnosis. We conduct extensive experiments based on two different mechanical systems to evaluate the proposed method. Comprehensive results and discussions demonstrate the promising performance of our approach. [ABSTRACT FROM AUTHOR]

To meet industrial demand, plenty of research works have been dedicated to monitoring the health status of planetary gearboxes. For the same purpose, a new path is explored based on rotating encoder signal (RES) by considering time-varying speed condition. In proposed method, generalized demodulation is firstly applied on second-order derivative of RES to eliminate the frequency variability caused by time-varying speed, and a combined comb filter is performed in this process to improve the signal-to-noise ratio of demodulated signal. The toothwise meshing information is then integrated as individual samples for the detection of local anomalies induced by gear defect. After reducing the dimensionality of these toothwise samples by reordered kernel principal component analysis, a normalized robust statistical distance is constructed to indicate the anomalies contained in RES. Simulation and experimental cases are provided to reveal the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

To realize fault identification of unlabeled data and improve model generalization capability, domain adaptation technology has been increasingly applied to intelligent fault diagnosis of machinery. Nevertheless, traditional domain adaptation diagnosis models generally restrict different domains to have the same label space, which does not always hold in complex industrial scenarios. Consequently, a more practical scenario, i.e., partial-set transfer diagnosis, is explored in this article, where the target label space is a subspace of the source domain. A multiweight domain adversarial network (MWDAN) is proposed to solve this issue, in which the weighting mechanism of class-level and instance-level is jointly designed to distinguish the label space and quantify the transferability of data samples. Based on the proposed strategy, the positive transfer between shared classes is promoted while the negative effect caused by outlier classes is circumvented. As a result, MWDAN can learn discriminative representations for accurate fault diagnosis in the target domain. Extensive experiments constructed on two mechanical systems demonstrate the outstanding performance of MWDAN. [ABSTRACT FROM AUTHOR]

The data distribution shift is inevitable in practical fault diagnosis due to internal and outside changes of equipment. These obstacles will lead to performance degrade or even failure of diagnostic models. In light of these problems, a novel unsupervised intelligent diagnostic framework named Adversarial Adaptation network based on Classifier Discrepancy (AACD) is introduced for mechanical fault diagnosis where the training and test domains have different data distribution. Specifically, AACD mainly contains two parts: one is the shared feature generator built by the 1-D convolutional neural network and the other is double task-specific classifiers. They play an adversarial training game to learn class-separable and domain-invariant features for fault diagnosis. The proposed AACD is evaluated by 15 transfer diagnosis tasks constructed on the planetary gearbox test-bed and the rolling bearing test-bed. Moreover, six popular algorithms are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Envelope demodulation based on vibration data is widely used for the fault detection of rolling element bearing, which yet largely relies on the high signal to noise ratio of signal. In practical scenarios, because of the existence of various interfering components, it is necessary to estimate the fault-sensitive frequency band for feature enhancement. However, many of current approaches are likely to produce misleading results for that they are robust only for part of these interferences. Facing with this problem, an alternative method, named as ALKurtogram, is proposed by aligning the frequency division strategy of traditional fast kurtogram (FK) with a new indicator, the averaged local kurtosis (ALK). ALK measures both the local and overall impulsiveness of objective component, which intrinsically makes up for some drawbacks of previous methods under multi-interference condition for fault feature extraction. The inherent nature of ALK also allows it to be compatible with the time-varying speed, hence more widespread industrial applications can be achieved. Furthermore, a harmonic energy index (HEI) is defined by following the result of ALKurtogram to ascertain the order of principle component for bearing status recognition. The effectiveness and superiority of proposed method are validated experimentally by comparing with FK, the results prove that it is a powerful tool for reliable monitoring of bearing. [ABSTRACT FROM AUTHOR]

Deep networks based mechanical intelligent diagnosis has been recently attracting considerable attentions with the development of Industry 4.0. Unfortunately, a more practical diagnostic scenario, i.e., unsupervised partial transfer diagnosis, has not yet been well addressed. In view of this, a novel unsupervised intelligent diagnosis framework named classifier inconsistency-based domain adaptation network is proposed in this article. In this approach, two discriminative one-dimensional convolutional networks are designed as the basic architecture. The source samples of the same categories as the target domain are then identified and emphasized to boost positive network training. Meanwhile, the classifier inconsistency is introduced to guide the model to learn discriminative and domain-invariant representations for the correct classification of unlabeled target data. Extensive experiments on two datasets are conducted to evaluate the proposed method. Additionally, five popular methods are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This article proposes a novel three-stage condition assessment scheme of rotating machinery using internal encoder data rather than traditional external vibration data. In this article, periodical group sparse derivatives (PGSD)-based signal denoising method is proposed to suppress the background noise, which incorporates the periodical group sparse derivative property of encoder signal and can be considered as an optimization problem. Moreover, the convexity condition of optimization problem is thoroughly investigated and an effective iterative algorithm is derived. After the PGSD-based signal denoising method, difference method (DM), and autoregressive (AR) filter are employed to convert the denoised encoder signal into intelligible speed information and remove the operation-related harmonic components. With the proposed method PGSD-DMAR, the periodical transient features are effectively extracted and the health condition of rotating machinery is successfully identified. The effectiveness and superiority of the PGSD-DMAR are verified via simulated signal and experimental data. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligent techniques have been extensively explored in the field of health monitoring and fault diagnosis due to their powerful capabilities. In this paper, we propose a deep coupled dense convolutional network (CDCN) with complementary data to integrate information fusion, feature extraction, and fault classification together for intelligent diagnosis. In this framework, built-in and external sensor data are first developed to form the input of network in parallel. Then, a one-dimensional CDCN is proposed, which not only could naturally build deeper network with alleviating the loss of features and gradient vanishing, but also develops a double-level information fusion strategy, including self-information fusion and mutual-information fusion, to facilitate the transmission of fault information and capture more comprehensive features. Finally, the extracted joint features are used for fault recognition and classification. The proposed approach is evaluated on a planetary gearbox test-bed. The results demonstrate the validity and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In the era of big data, a huge amount of monitoring and manufacturing data is generated every hour. As these data are typically measured from different machines and under different working regimes, prior information and domain knowledge are highly required in order to properly analyze and utilize these data. In view of this limitation, a data-driven self-comparison approach is proposed for the monitoring of rotating machinery. In this approach, comb filtering is introduced to extract the concerned signals from multisource background noise. A Gini-guided residual singular value decomposition is then proposed to enhance local anomalies induced by early defects. Finally, an iterative Mahalanobis distance is constructed to measure the statistical deviation of monitored component from a normal state. With the proposed method, health monitoring of rotating machinery could be achieved without prior information and domain knowledge, thereby providing an automatic data processing and condition monitoring tool in big data context. [ABSTRACT FROM AUTHOR]