Under the international background of the transformation and promotion of manufacturing, the Chinese government proposed the “Made in China 2025” strategy, which focused on the improvement of a quality-based innovation ability. Moreover, predicting manufacturing quality is one of the crucial measures for quality management. Accurate prediction is closely related to the feature learning of manufacturing processes. Therefore, two categories of intelligent learning approaches, i.e., shallow learning and deep learning, are investigated and compared for manufacturing quality prediction in this paper. Specifically, the feed forward neural network (FFNN) with one hidden layer and the least squares support vector machine (LSSVM) with no hidden layers are selected as the representatives for shallow learning, and the deep restricted Boltzmann machine (DRBM) and the stack autoencoder (SAE) are chosen as the representatives for deep learning. The manufacturing data is collected from a competition about manufacturing quality control in the Tianchi Data Lab of China. The experiments show that the deep framework overwhelms the shallow architecture in terms of mean absolute percentage error, root-mean-square error, and threshold statistics. In addition, the prediction results also indicate that the performances depend on the length of the training data. That is, the bigger the sample size is, the better the performance is.

An echo state network (ESN) is a type of recurrent neural network that is good at processing time-series data with dynamic behavior. However, the use of ESNs to enhance fault-classification accuracy continues to be challenging when the condition signals are collected by low-cost sensors. In this paper, a deep network algorithm, called a deep hybrid state network (DHSN), is proposed for fault diagnosis of three-dimensional (3D) printers using attitude data with low measurement precision. In the DHSN, the output data of a sparse auto-encoder (SAE) are regarded as the abstract features of a double-structured echo state network (DESN). The DESN is designed for feature reinforcement and fault recognition, wherein the first function reinforces the features and the second is used for fault classification. More specifically, feature reinforcement is developed to improve the clustering performance and replace the traditional overall feedback fine-tuning in deep models. This strategy improves learning efficiency and overcomes the vanishing-gradient problem for deep learning. The forecasting performance of the proposed approach is evaluated in experiments, and its superiority is demonstrated through comparison with other intelligent fault diagnosis technologies.

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

In industrial applications, different abnormal conditions of a machine or a machinery fleet typically occur sequentially instead of becoming initially known. Therefore, one of the most challenging tasks for the machinery fault diagnosis is to incrementally classify unknown abnormal patterns. For this reason, an incremental novelty identification (INI) method is proposed, on the strength of deep learning-based representation learning, novelty detection and incremental learning. In the INI framework, a convolutional neural network autoencoder is first developed for high-level feature extraction from multiple monitoring signals. An online sequential-extreme learning machine (OS-ELM) is then proposed to learn healthy features of the normal condition initially known. The newly collected data are employed for updating the OS-ELM with the quantification of the model modification for novelty detection. An incremental learning procedure is implemented by adding a new OS-ELM model representing the novel abnormal pattern. The repository of OS-ELMs, each of which represents a specific condition, is employed for incrementally diagnosing known abnormal patterns with the identification of unknown ones. The performance of the proposed INI is validated under experimental scenarios considering both a benchmark bearing case study and a delta three-dimensional printer, and the results are compared with other state-of-the-art approaches.

Effective application of fault diagnosis models requires that new fault types can be recognized rapidly after they occur few times, even only one time. To this end, a self-adaptation graph attention network via meta-learning (SGANM) is proposed. Specifically, based on a collected large-scale labeled dataset containing abundant disjoint categories (i.e., any of the categories in the target diagnosis task is not contained), meta-learning is used to train a meta-learner across abundant randomly generated meta-tasks, and the meta-learner can rapidly generalize to the target fault diagnosis task containing only few labeled samples. To full exploitation of the relationships among the samples in the support set and query set of each meta-task, an SGAN is designed to realize the meta-learner. The effective strategies including spatial–temporal graph-based node initial embedding, 2-D edge embedding, and multihead masked attention mechanism-based embedding propagating make the proposed meta-learner have powerful meta-knowledge learning ability. Experiments are conducted on a benchmark dataset and a dataset collected from a practical experimental platform, and competitive performance has been achieved by the proposed SGANM compared to the other few-shot learning (FSL) algorithms.

Bearing condition is a non-negligible part of mechanical equipment health monitoring. Most of the existing bearing fault diagnosis methods are based on the premise that all data classes are known and lack the capability of incremental diagnosis of fault modes. However, in engineering practice, the initial monitoring data only provide normal condition, and the subsequent data of different classes of faults are collected gradually. To address this practical problem, we propose incremental contrastive learning of homologous and interclass features for bearing to achieve incremental diagnosis of bearing fault modes from single to multiple classes. Important homologous and interclass features of bearings are first extracted by contrastive learning. The obtained features are then employed to establish a distance threshold for the anomaly diagnosis of subsequent samples. Upon appearing anomalies incrementally up to a certain amount, novel classes are upgraded and fed back to the model. In this way, new class data are treated as incremental learning resources. The proposed method was evaluated using both benchmark bearing and gearbox bearing experiments. Results show supreme diagnostic performance compared to peer state-of-the-art approaches. The present method is intrinsic in extracting homologous and interclass features for practical bearing fault diagnostics.