Failure mode (FM) recognition and remaining useful lifetime (RUL) prediction play pivotal roles in the field of prognostics and health management (PHM), particularly in regard to monitoring machinery degradation. Advances in sensor technology have opened avenues for FM recognition and RUL prediction by leveraging data from multiple sensors. However, previous research has exhibited certain limitations. Some studies have taken all multisensor data as direct inputs, overlooking the potential heterogeneity in the relevance of individual sensor data to machinery degradation. Others have relied on visual inspection and subjective assessments for sensor selection. These approaches struggle to adaptively select sensors, especially in scenarios involving multiple FMs and operating conditions (OCs), and when only partial FM labels are available. To address these challenges, this paper proposes a novel deep-learning network that adaptively selects sensors to jointly recognize FMs and predict RUL under time-varying OCs. The core of this network incorporates an attention-based long short-term memory (LSTM) module. Within this module, adaptive sensor selection weights are generated, leading to the accurate recognition of FMs and the precise prediction of the RUL. In the context of model training, we construct loss functions utilizing semilabeled samples and extract OC-invariant features through domain adaptation, enhancing the accuracy of FM recognition and RUL prediction. To assess the effectiveness and the generalizability of the proposed method, numerical experiments and two case studies involving aircraft engines and bearings are conducted. Note to Practitioners—This paper proposes a deep-learning network designed for adaptive sensor selection in the context of semisupervised FM recognition and RUL prediction, particularly in scenarios involving multiple OCs. To operationalize this method, five key steps are outlined: First, Data Collection: data are gathered from multiple sensors, time-varying OCs, multiple FMs, and failure time of historical units. It is important to note that FM data are available for only a subset of historical units. Second, Data Preprocessing: The sensor data are tailored using the sliding time window technique, generating a substantial number of samples from historical units. Third, Network Construction: Subsequently, the adaptive sensor selection network is constructed. Fourth, Model Training: Loss functions are constructed, and model parameters are estimated through end-to-end training. Finally, Model Application: The method is utilized to recognize FMs and predict RUL for in-service units. This deep learning network can be applied effectively to various degraded machines experiencing multiple FMs and OCs. It is particularly suitable for machines with complex physical mechanisms or unknown failure thresholds.

With advancements in sensor technology, high dimensional signals such as functional curves and images are typically collected from multiple sensors to characterize the degradation of a system. Data fusion methods are employed to integrate multisensor signals generated from the system into a scalar health index (HI) to understand the degradation status of the system. This paper develops sparse group LASSO-principal component analysis (SGL-PCA), a method that constructs HIs for image and profile data. First, we remove the smooth background from each sensor signal. Then, we solve the degradation patterns and the degradation paths through a rank-one matrix approximation problem, with the consideration of the sparsity of the measurements related to the degradation process and the monotonicity of the degradation paths. Results from a simulation study and a case study illustrate that the HI constructed by the proposed method outperforms the benchmark methods in identifying the measurements subject to the degradation process and predicting the remaining useful life of the system. Note to Practitioners—In practice, sensors generating multiple high-dimensional curves and images are often installed in systems to characterize their degradation status. Compared with scalar sensor signals, the information that associates with the degradation process often appears in sparse regions from the sensor signals. Therefore, identifying the degradation information accurately is important in the health index (HI) construction for degradation modeling and prognostic analysis. This article proposes a method that simultaneously selects the degradation information and estimates the optimal weights for integrating multi-sensor signals in constructing the HI. The proposed method is applicable in the case where the systems degrade under a single failure mode, and multiple sensors are used to monitor the degradation processes. Practitioners can implement our method to predict the remaining useful lives of in-service systems through three steps: (1) estimate the backgrounds of each sensor and derive data-fusion model using a historical dataset; (2) construct the HIs of in-service systems; (3) predict the remaining useful lives of these systems based on the developed HIs.

The monitoring of data streams with a network structure have drawn increasing attention due to its wide applications in modern process control. In these applications, high-dimensional sensor nodes are interconnected with an underlying network topology. In such a case, abnormalities occurring to any node may propagate dynamically across the network and cause changes of other nodes over time. Furthermore, high dimensionality of such data significantly increased the cost of resources for data transmission and computation, such that only partial observations can be transmitted or processed in practice. Overall, how to quickly detect abnormalities in such large networks with resource constraints remains a challenge, especially due to the sampling uncertainty under the dynamic anomaly occurrences and network-based patterns. In this paper, we incorporate network structure information into the monitoring and adaptive sampling methodologies for quick anomaly detection in large networks where only partial observations are available. We develop a general monitoring and adaptive sampling method and further extend it to the case with memory constraints, both of which exploit network distance and centrality information for better process monitoring and identification of abnormalities. Theoretical investigations of the proposed methods demonstrate their sampling efficiency on balancing between exploration and exploitation, as well as the detection performance guarantee. Numerical simulations and a case study on power network have demonstrated the superiority of the proposed methods in detecting various types of shifts. Note to Practitioners—Continuous monitoring of networks for anomalous events is critical for a large number of applications involving power networks, computer networks, epidemiological surveillance, social networks, etc. This paper aims at addressing the challenges in monitoring large networks in cases where monitoring resources are limited such that only a subset of nodes in the network is observable. Specifically, we integrate network structure information of nodes for constructing sequential detection methods via effective data augmentation, and for designing adaptive sampling algorithms to observe suspicious nodes that are likely to be abnormal. Then, the method is further generalized to the case that the memory of the computation is also constrained due to the network size. The developed method is greatly beneficial and effective for various anomaly patterns, especially when the initial anomaly randomly occurs to nodes in the network. The proposed methods are demonstrated to be capable of quickly detecting changes in the network and dynamically changes the sampling priority based on online observations in various cases, as shown in the theoretical investigation, simulations and case studies.

Tensor data with rich structural information become increasingly important in process modeling, monitoring, and diagnosis in manufacturing medical and other applications. Here structural information is referred to the information of tensor components such as sparsity, smoothness, low-rank, and piecewise constancy. To reveal useful information from tensor data, we propose to decompose the tensor into the summation of multiple components based on their different structural information. In this article, we provide a new definition of structural information in tensor data. We then propose an additive tensor decomposition (ATD) framework to extract useful information from tensor data. This framework specifies a high dimensional optimization problem to obtain the components with distinct structural information. An alternating direction method of multipliers (ADMM) algorithm is proposed to solve it, which is highly parallelable and thus suitable for the proposed optimization problem. Two simulation examples and a real case study in medical image analysis illustrate the versatility and effectiveness of the ATD framework. Note to Practitioners—This article was motivated by a real case in medical imaging: extracting aortic valve calcification (AVC) regions from the tensor data obtained from computed tomography (CT) image series of the aortic region. The main objective is to decompose image series into multiple components corresponding to tissues, calcium deposition, and error. Similar needs are pervasive in other medical image analysis applications as well as the image-based modeling, monitoring, and diagnosis of industrial processes and systems. Existing methods fail to incorporate a detailed description of the properties of image series that reflect the physical understanding of the system in both the spatial and temporal domains. In this article, we provide a systematic description of the properties of image series and use them to develop a decomposition framework. It is applicable to various applications and can generate more accurate and interpretable results.

Fourier-transform infrared spectroscopy (FTIR) is a widely adopted technique for characterizing the chemical composition in many physical and chemical analyses. However, FTIR spectra are subject to multiple sources of uncertainty, and thus the analysis of them relies on domain experts and can only lead to qualitative conclusions. This study aims to analyze the effect of a certain treatment on FTIR spectra subject to two commonly observed uncertainties, the offset shift and the multiplicative error. Due to these uncertainties, the pre-exposure FTIR spectra are modeled according to the physical understanding of the uncertainty—observed spectra can be viewed as translating and stretchering an underlying template signal, and the post-exposure FTIR spectra are modeled as the translated and stretchered template signal plus an extra functional treatment effect. To provide engineering interpretation, the treatment effect is modeled as the product of the pattern of modification and its corresponding magnitude. A two-step parameter estimation algorithm is developed to estimate the underlying template signal, the pattern of modification, and the magnitude of modification at various treatment strengths. The effectiveness of the proposed method is validated in a simulation study. Furtherly, in a real case study, the proposed method is used to investigate the effect of plasma exposure on the FTIR spectra. As a result, the proposed method effectively identifies the pattern of modification under uncertainties in the manufacturing environment, which matches the knowledge of the affected chemical components by the plasma treatment. And the recovered magnitude of modification provides guidance in selecting the control parameter of the plasma treatment.Note to Practitioners—FTIR spectrometer is often used to characterize the surface chemical composition of a material. Due to the large uncertainties associated with the nature of spectrometer and the measurement environment, the FTIR signals are usually examined visually by experienced engineers and technicians in industrial applications, which can be both time-consuming and inaccurate. To understand the effect of plasma exposure on the surface property of carbon fiber reinforced polymer (CFRP) material, the elimination of uncertainties associated with FTIR signals is investigated, and a systematic method is proposed to quantify the effect of surface treatments on FTIR signals. A two-step analytic procedure is proposed, which provides information on how the plasma exposure distorts the FTIR signals, and how the plasma distance relates to the magnitude of the distortion. The methodology in this article can be used to analyze the treatment effect on a variety of spectroscopic measurements that are subject to uncertainties such as offset and scaling errors, which expands the applications of in situ handheld spectrometer metrology in manufacturing industries.

Additive manufacturing (AM) or 3-D printing refers to a new class of technologies that actively construct products directly from any 3-D digital model. In the future, the broader applications of AM will require a cost reduction of AM machines. Currently, the products fabricated by low-end machines, such as those fabricated using fused deposition modeling (FDM) processes, suffer from the issue of low dimensional accuracy due to multiple error sources. To properly manage error sources for improved prevision, this paper proposes a novel strategy for error compensation in the FDM processes. First, we consecutively attribute the dimensional inaccuracy to two major error sources that affect the geometric shape of the product: 1) positioning error of the extruder and 2) shape deformation induced by processing error, including material phase change and other variations that occur. The extruder positioning error is characterized by a Kriging model, while the modeling of shape deformation due to processing error follows the method developed by Huang et al. Second, using error equivalence concept, we transform the positioning error into the equivalent amount of design input error. Finally, we adjust the design to compensate for the overall shape deviation. To validate this strategy, we conduct a designed experiment for the shape deviation prediction and the compensation. The experimental results successfully demonstrate the effectiveness of the proposed three-step strategy to manage multiple error sources in the FDM processes.