In real industrial applications, intelligent methods are recently emerging for remaining useful life (RUL) prediction. However, their development is hindered by two obstacles. First, it is hard for an ordinary edge client to achieve RUL prediction owing to its weak computing capacity and limited data volume. Second, as for all edge clients, it is unrealistic to share data with each other due to the potential conflict of interests. Therefore, a federated learning-based RUL prediction method namely FedRUL is proposed to solve these problems. In this method, multiple edge clients and a cloud server are applied to train a global encoder and an RUL predictor without data sharing. Each client includes a convolutional autoencoder (CAE) comprised of an encoder and a decoder while the server includes a same encoder and an RUL predictor. During each epoch, CAEs are trained in all clients through their corresponding local training datasets first. Then, all local encoders are uploaded to the server and aggregated to a global encoder through assigning weights for all clients according to their performance on a validation dataset in the server. At last, this global encoder is sent back to all clients for extracting low-level features from their datasets and uploading these features to train the RUL predictor for each client one by one. Two experiments suggest FedRUL offers a promising solution on confidential decentralized learning for RUL prediction.

With the coming of the industrial Big Data era, data-driven fault diagnosis models emerge recently and show potential results in many studies. However, it is impractical to collect massive high-quality data in actual industrial applications, which strongly reduces the performance of fault diagnosis models. Therefore, multiple data owners are motivated to share their own data for training a brilliant model with the aggregated dataset. Notwithstanding, it is unrealistic to share data due to the potential conflict of interest. For addressing the issue of data island, a new federated framework namely FedCAE is proposed for fault diagnosis. In this framework, each client is equipped with a convolutional autoencoder (CAE) trained by its corresponding local data. Then, all CAEs are uploaded to a server and aggregated to a global CAE according to an adaptive weighted selection mechanism. Afterwards, the global CAE is downloaded to all clients for extracting low-level features from local datasets by its encoder and uploading these features to train a global fault diagnosis classifier in the server. At last, the classifier is downloaded to all clients for completing their own diagnosis tasks. Experiment results on two bearing datasets suggest FedCAE offers a promising solution on confidential decentralized learning.

Artificial intelligence-based machinery fault diagnosis techniques have been increasingly considered in many industrial fields. The convolutional neural network (CNN) is able to learn features from raw signals because of its filter structure. Thus, several studies have applied CNN based methods for machinery fault recognition and classi-fication. However, most of these studies are based on a balanced dataset, while ignoring that normal data and fault data tend to be highly imbalanced in real-world applica-tions. Conventional CNNs do not work well for highly im-balanced fault diagnostics tasks and often lead to the degradation of performance. Therefore, in this paper, a learning framework called deep focus parallel convolu-tional neural network (DFPCN) is proposed to overcome the weakness. It has powerful feature learning capabilities due to its parallel convolutional architecture. A new loss function named adaptive cross entropy (ACE) loss is de-signed for the DFPCN to focus training on minority health condition samples which are hard to classify. The effec-tiveness and superiority of the proposed DFPCN are vali-dated by a highly imbalanced dataset constructed from bearing vibration signals. The diagnostics results demon-strate that DFPCN outperforms the state-of-the-art CNN based methods in terms of accuracy and stability, and avoids adding computational burden with the redundant samples compared with oversampling methods.

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

A milling cutter is one of the most important parts of machine tools. Its working status significantly influences the precision of workpiece. Due to the complex wear mechanism, the single sensor may be difficult to acquire the complete degradation information of milling cutters. Therefore, in this article, a feature learning based method is proposed to automatically extract features from multisource data and predict the remaining useful life of cutting tools in real time. First, a statistic-based method is constructed to detect and delete the outliers hidden in the monitoring data. Second, the clean data are input into a multiscale convolutional attention network (MSAN) to learn features and fuse multisource data. At last, the fused data are used to predict the remaining useful life of cutting tools in a regression layer. Compared with traditional tool life prediction methods, the proposed method is able to fuse multisource data through an attention feature learning model to conduct the life prediction of tools. Additionally, the data cleaning and model optimization methods are also proposed to promote engineering practicability. To validate the effectiveness of such method, the life testing experiments on milling cutters are conducted to obtain run-to-failure data. In those experiments, multisensor monitor data are acquired, which are used to conduct validation experiments testing the effectiveness of the proposed method. The results indicate the superiority of the proposed method in remaining useful life prediction milling cutters.

A rough environment or unexpected accident of data acquisition instrument can introduce some anomalies in monitoring data. Those anomalies reduce data quality and lead to the incorrect recognition of machine health status. However, the research on anomaly detection of machine monitoring data (MMD) is very scarce. Moreover, anomaly detection methods in other fields cannot be directly applied to MMD. Therefore, a robust anomaly detection method called similarity-measured isolation forest (SM-iForest) is proposed to detect abnormal segments and the data therein. The inadaptability and instability of iForest were reduced while processing MMD benefiting from the characteristics of sliding-window processing. Moreover, an anomaly identification stage measuring the relative similarity of possible abnormal segments further improved the robustness of iForest. The effectiveness of the proposed method was verified with a vibration simulation signal and three sets of milling force signals. The results demonstrate that SM-iForest can detect the missing, shifting, and swelling segments robustly. Detection results of comparing seven methods suggest that SM-iForest is a promising method to detect MMD anomaly with a high detection rate and low false alarm rate.