Streaming data of machines is continuously collected in practical applications, which produces new fault information with respect to the health change. Therefore, a lifelong-learning intelligent diagnosis model is desired for new fault type recognition based on the streaming data. However, existing research in intelligent fault diagnosis always treats new fault type detection and class incremental learning as two independent problems, which reduces their practicality in industrial applications. To tackle this limitation, a graph continual learning network is constructed for incremental intelligent diagnosis of new faults. The method integrates the advantages of both new fault type detection and class incremental learning. In the method, a graph convolutional network (GCN) based model is formulated for detecting new classes to prejudge whether the DL model needs to be updated. Once any new class is detected, class incremental learning is started automatically to update the DL model without leading to catastrophic forgetting. The proposed method is applied to a pump fault diagnosis case with incremental fault types. Results show that the proposed method offers an effective solution for online intelligent fault diagnosis with satisfactory classification performanceNote to Practitioners—Existing DL-based intelligent diagnosis models often assume the closed-set assumption, i.e., fault types of the monitoring data are the same as those of the training data. In the situations where the assumption is held, DL models receive high-precision recognition results towards instances from the monitoring data stream. However, when a new fault pattern appears in the monitoring data stream, the previous well-trained diagnosis model will inevitably classify the instances into one of the known patterns, resulting in untrustworthy results. This paper proposes a method with the aim of tackling the above issue. The method is able to realize the following functions: Once the instances in the streaming data are detected as a new fault pattern, the class incremental learning will be started automatically. The detected class is used to update the diagnosis model. On the contrary, if the instances are not detected as a new class, they will be sent to the diagnosis model for fault recognition. An incremental diagnosis task is designed under a centrifugal pump application scenario. The results demonstrate the feasibility of the proposed method. The method is anticipated with more application scenarios to verify its superiority.

The increasing complexity and interconnectivity of modern industrial machinery, together with persistent demands for operational efficiency, have elevated reliable remaining useful life (RUL) prediction to a cornerstone of industrial intelligence. To this end, multimodal monitoring has been widely adopted, as it provides complementary perspectives on system health. Although numerous studies have exploited multimodal data to enable holistic condition assessment, most existing approaches remain fundamentally deterministic, yielding single-point estimates that are often overconfident and potentially misleading-particularly in safety-critical or cost-sensitive scenarios. To fill this trustworthy gap, a multimodal evidential learning framework is proposed with uncertainty-calibrated fusion. It integrates heterogeneous monitoring modalities by jointly exploiting scarce labeled run-to-failure trajectories and abundant unlabeled operational data. Each modality is modeled using a high-order evidential distribution, which enables an explicit analytical decomposition of predictive uncertainty into aleatory (data-driven) and epistemic (model-driven) components. These modality-specific evidential representations are subsequently fused through an uncertainty-aware mechanism. Experiments on multimodal run-to-failures of robotic harmonic drives validate the proposed framework's performance in both predictive accuracy and uncertainty quantification. Furthermore, ablation studies and comprehensive comparisons with state-of-the-art methods substantiate the contributions of individual modules and confirm the overall framework's suitability as a trustworthy decision-support tool for industrial applications.

Accurate prediction of machinery’sremaining useful life (RUL) is essential for preventing catastrophic breakdowns and supporting predictive maintenance. Although RUL prediction has been extensively studied, most literature develops on unimodal data, which providesa limited and often biased perspective. Multimodal monitoring, which collects multiple sensor data, enables a more comprehensive understanding of degradation processes. While promising, significant challenges are encountered in existing methods: 1) point yet deterministic predictions are predominantly produced which, while potentially erroneous, tend to exhibit overconfidence, thereby lacking the dynamic uncertainty informing; 2) the processing of heterogeneous data and the achievement of physically interpretable fusion remain challenging; and 3) anomalies in the operation process are not appropriately identified. To address these issues, a new multimodal uncertainty-aware RUL prediction framework is proposed, grounded in stochastic modeling. Fractional stochastic differential equation-controlled subnets process each modality independently, wherein layer-wise transformations are modeled as state evolution in stochastic dynamical systems, allowing modality-specific uncertainty to be quantified without requiring parameter priors. A Lagrange multiplier-based fusion module is subsequently employed to perform explicit uncertainty-based fusion, enabling an interpretable and synergistic integration. Validation on harmonic drive reducers for robots demonstrates the superiority of the proposed framework, achieving an average improvement of 26.6% in RMSE and a 16.6% reduction in MAPE compared to state-of-the-art benchmarks. Furthermore, the method significantly reduces prediction uncertainty variance by 21.3%, offering more reliable insights into system degradation.

Accurate machinery health prognosis, also known as remaining useful life (RUL) prediction, is critical for preventing catastrophic accidents and implementing predictive maintenance strategies. This makes it a highly attractive research area. Many existing studies have been developed on unimodal data, yet such data can only provide a restricted perspective and incomplete health state monitoring. Some researchers seek to address this issue from a multimodal standpoint. While promising, these methods still have certain shortcomings: 1) the imbalance for unlabeled and low-quality data compared to well-labeled data is not considered, causing their potential underexploited; 2) information richness during fusion is insufficient, discarding many valuable original and subtle health state cues, and they fail to timely tackle unexpected online anomalies; and 3) correlations and complementary information across modalities are neglected. To address these challenges, a multimodal correlation-aware fusion framework is proposed for machinery health prognosis. The framework adopts a pretrain-finetune paradigm with two parts. The first part achieves effective exploitation of the unlabeled and low-quality multimodal data pieces. The second part, through degradation pattern recognition, enables the framework to bridge the gap between scarce multimodal labeled data and accurate RUL prediction. A real industrial multimodal dataset of milling cutters is applied to demonstrate the proposed framework. Results from a series of ablation experiments and comparisons with state-of-the-art prediction methods indicate the effectiveness of each key component within the framework and its overall superiority. The framework shows promise in adapting to more downstream industrial tasks, providing accurate and reliable insights from limited data resources.

Condition monitoring has played a significant role in reducing downtime and maintenance costs for key rotating machinery. However, traditional methods to power wireless sensor nodes depend highly on capacity-limited batteries or wiring from external source. Although the rotational energy harvesting technologies have been widely considered as a promising self-powered method, the output power under low-frequency occasions fails to supply the usable energy to wireless sensor nodes for condition monitoring. Therefore, a self-powered wireless condition monitoring system for rotating machinery in low-frequency occasions is presented in this article. A variable reluctance energy harvester is designed to convert rotational motion into electrical power. The ring-shaped stator contains magnets and tile silicon steel, while the rotor is composed of teethed silicon steel and coils. Besides, a ring-shaped circuit for low-speed occasions is designed to achieve power management and storage, signal detection and wireless transmission. In addition, an experimental test is carried out to verify the performance of the proposed self-powered wireless condition monitoring system. The results show that the output power of the proposed harvester reaches 336.7–851.8 mW at 200–328 rpm, while the average power after rectification and filtering is 203.3–602.5 mW. Moreover, the power test results show that the broadcasting, connecting, collecting and transmitting, and sleep modes of WiFi consume around 450, 206, 313, and 46 mW, respectively. By properly prolonging sleep mode, the average power consumption of wireless sensor networks can be significantly reduced. Furthermore, the condition monitoring performance of the proposed self-powered system is verified by acceleration detection.

Event-based cameras are the emerging bioinspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, etc. While the event-based cameras have been initially exploited in several common vision-based tasks in the recent years, the investigation on machine condition monitoring problem is quite limited. This article offers the first attempt in the current literature on exploring the contactless event vision data for machine fault diagnosis. A vibration event representation is proposed to transform the event records into typical data samples, and a deep convolutional neural network model is used for processing the event information. To enhance the model robustness against environmental noisy vision events, an event data augmentation method is proposed to introduce variations of the event patterns. A deep representation clustering method is further proposed to improve the pattern recognition performance with respect to different machine health conditions. Experiments on the event vision-based rotating machine fault diagnosis problem are carried out. It is extensively validated that high fault diagnosis accuracies can be obtained using the vision data from the event-based cameras, which are competitive with the popular accelerometer data. Considering the properties of flexibility, portability, and data recognizability, the event-based cameras thus provide a promising new tool for contactless machine health condition monitoring and fault diagnosis.

Machinery often operates under time-varying conditions, which can lead to distribution discrepancies in degradation samples. However, most existing domain generalization-based methods for predicting remaining useful life (RUL) are applied to constant operation conditions, and they may demonstrate performance deteriorations across alternating operation conditions. This article proposes an optimal-subdomain generalization method for RUL prediction. It considers the local fluctuations of monitoring signals under time-varying operation conditions and performs subdomain generalization to overcome the distribution discrepancies of samples. First, the RUL labels are discretized into pseudolabels representing different health states. To guide the discretization, an optimal discretization strategy is proposed to establish the relationship between the generalization error and degradation samples. Second, the optimal subdomain generalization model is utilized to extract subdomain representations among different operation conditions for the estimation of RULs. Finally, the run-to-failure experiments on hub-bearings are conducted for demonstration. The prediction results with high accuracy show advantages of the proposed method for RUL prediction.

Uncertainty quantification of the remaining useful life (RUL) for degraded systems under the big data era has been a hot topic in recent years. A general idea is to execute two separate steps: deep-learning-based health indicator (HI) construction and stochastic process-based degradation modeling. However, there exists a critical matching defect between the constructed HI and a degradation model, which seriously affects the RUL prediction accuracy. Toward this end, this article proposes an interactive prognosis framework between deep learning and a stochastic process model for the RUL prediction. First, we resort to stacked contractive autoencoders to fuse multiple sensor information of historical systems for constructing the HI in a typical unsupervised manner. Then, considering the nonlinear characteristic of the constructed HI, an exponential-like degradation model is introduced to construct its degradation evolving model, and theoretical expressions of the prediction results are derived under the concept of the first hitting time. Furthermore, we design an optimization objective function by integrating the HI construction and degradation modeling for the RUL prediction. To minimize the designed objective function of the proposed interactive prognosis framework, a gradient descent algorithm is employed to update the model parameters. Based on the well-trained interactive prognosis model, we can obtain the HI of a field system from stacked contractive autoencoders with sensor data and the probability density function (pdf) of the predicted RUL on the basis of the estimated parameters. Finally, the effectiveness and superiority of the proposed interactive prognosis method are verified by two case studies associated with turbofan engines.

Many deep-learning methods have been developed for fault diagnosis. However, due to the difficulty of collecting and labeling machine fault data, the datasets in some practical applications are relatively much smaller than the other big data benchmarks. In addition, the fault data come from different machines. Therefore, on some occasions, fault diagnosis is a multidomain problem with small data, where satisfactory transfer performance is difficult to obtain and has been rarely explored from the few-shot learning viewpoint. Different from the existing deep transfer learning solutions, a novel transfer relation network (TRN), combining a few-shot learning mechanism and transfer learning, is developed in this study. Specifically, the fault diagnosis problem has been treated as a similarity metric-learning problem instead of solely feature weighted classification. A feature net and a relation net have been, respectively, constructed for feature extraction and relation computation. The Siamese structure has been borrowed to extract the features of the source and the target domain samples with shared weights. Multikernel maximum mean discrepancy (MK-MMD) is employed on several higher layers with different tradeoff parameters to enable an efficient domain feature transfer considering different feature properties. To implement efficient diagnosis based on small data, an episode-based few-shot training strategy is adopted to train TRN. Average pooling has been adopted to suppress the noise influence from the vibration sequence which turns out to be important for the success of time sequence-based fault diagnosis. Transfer experiments on four datasets have verified the superior performance of TRN. A significant improvement of classification accuracy has been made compared with the state-of-the-art methods on the adopted datasets.

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

Condition monitoring of machinery has entered the big data era, while the existence of dirty data reduces the quality of the whole data. In order to recognize the dirty data included in machinery monitoring data, a new method is proposed in this paper. First, a feature named sampled power index (SPI) is designed to transform the dirty data recognition issue into the outlier recognition. Then the windowing technique, the difference operation and the logarithm transform are introduced to reduce the feature tendency and the feature volatility. Next, auto regression-generalized autoregressive conditional heteroskedasticity (AR-GARCH) model is applied to regress the feature series and produce the crippled local means and local volatilities. Finally, the features are normalized and the 3σ criterion is applied to recognize the dirty data. The performance and the feasibility of this proposed method are evaluated by a simulation and an experiment. The results validate the effectiveness of the proposed method.

Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input data of MRVM for the classification of different health conditions of machines. The proposed method is applied to the fault diagnosis of locomotive rolling bearings. According to the diagnosis results, it is verified that the proposed method is able to reliably classify different health conditions. By comparing with diagnosis method based on time-domain statistical analysis and wavelet transformation, the proposed method shows its superiority in automatic features extraction from raw signals.

Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.