Data-driven predictive model of reliability estimation using degradation models: a review

Data-model interactive prognosis for multi-sensor monitored stochastic degrading devices

This article presents an idea of achieving reliability through Industrial Internet of Things (IIoT) for industrial power systems. It proposes hybrid approach for predictive and corrective maintenance. It discusses the self-corrective maintenance (SCM) paradigm as hybrid approach for industrial power systems along with condition-based maintenance approach utilizing IIoT to achieve it. As it is well known that industries pay huge penalty for the down time, and suffer to meet reliability demands for years. Study witnesses its cost in millions of dollars yearly for production disruptions. It can be prevented by proactively following the aggressive maintenance schedule. However, it often becomes expensive as part or service may not be utilized for its full life and failure may occur even in middle of maintenance cycle. On the other hand, condition-based maintenance (CBM) helps utilize the full life and prevents the downtime by predicting the failures ahead. This article reviews current maintenance practices followed by industry leaders and a proposal on self-corrective maintenance based on condition of restorable resources. It is about learning the condition of subsystems by itself and taking corrective action when subsystem is not active. This concept helps reduce manual intervention to correct the problem as well as the maintenance cost. This research also covers the self-uncorrectable issues to be handled by proactively following CBM process through IIoT. This hybrid proposal could be a significant gear shift in maintenance direction for general industry as well as power systems. It can be termed as industry's 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> revolution or Industry 5.0.

Hybrid platforms are based on the new technologies of the fourth industrial revolution such as IIoT and cloud computing, which allow local and remote operation. A hybrid system presents a low cost of operation and maintenance. This paper describes the development of a hybrid IIoT (Industrial Internet of Things) & Arduino IoT (Internet of Things) Cloud platform for the analysis of continuous industrial process data in real time through the cloud. This procedure offers a more stable and secure control through the new Cloud technology. The IIoT platform consists of industrial equipment and sensors that are controlled through Delta IIoT devices. An Arduino IoT Cloud with ESP32 module has the functionality of a router that allows sending data to the cloud, thereby achieving the control and monitoring of level and temperature variables of a continuous process. With this type of architecture, the industrial sector has great opportunities to improve its competitiveness by being able to analyze large amounts of data collected from sensors. In addition, the hybrid platform guarantees the visibility of the variables in real time from anywhere in the world where connectivity exists. The results are presented in graphs and are promising as there is no data loss in the cloud and data latency is almost negligible.KeywordsIndustrial Internet of Things (IIoT)Internet of Things (IoT)Industry 4.0Hybrid IIoT

Federated learning for green and sustainable 6G IIoT applications

The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system.

The degradation of rolling-element bearings is mainly stochastic due to unforeseeable influences like short term overstraining, which hampers the prediction of the remaining useful lifetime. This stochastic behaviour is hardly describable with parametric degradation models, as it has been done in the past. Therefore, the two prognostic concepts presented and examined in this paper introduce a nonparametric approach by the application of a dynamic Gaussian Process (GP). The GP offers the opportunity to reproduce a damage course according to a set of training data and thereby also estimates the uncertainties of this approach by means of the GP’s covariance. The training data is generated by a stochastic degradation model that simulates the aforementioned highly stochastic degradation of a bearing fault. For prediction and state estimation of the feature, the trained dynamic GP is combined with the Unscented Kalman Filter (UKF) and evaluatedin the context of a case study. Since this prognostic approach has shown drawbacks during the evaluation, a multiple model approach based on GP-UKF is introduced and evaluated. It is shown that this combination offers an increased prognostic performance for bearing fault prediction.

Data-driven condition-based maintenance (CBM) can be an effective predictive maintenance strategy for components within complex systems with unknown dynamics, nonstationary vibration signatures or a lack of historical failure data. CBM strategies allow operators to maintain components based on their condition in lieu of traditional alternatives such as preventive or corrective strategies. In this paper, the authors present an outline of the CBM program and a field pilot study being conducted on the gearbox, a critical component in an automated cable-driven people mover (APM) system at Toronto’s Pearson airport. This CBM program utilizes a paired server-client “two-tier” configuration for fault detection and prognosis. At the first level, fault detection is performed in real-time using vibration data collected from accelerometers mounted on the APM gearbox. Time-domain condition indicators are extracted from the signals to establish the baseline condition of the system to detect faults in real-time. All tier one tasks are handled autonomously using a controller located on-site. In the second level pertaining to prognostics, these condition indicators are utilized for degradation modeling and subsequent remaining useful life (RUL) estimation using random coefficient and stochastic degradation models. Parameter estimation is undertaken using a hierarchical Bayesian approach. Degradation parameters and the RUL model are updated in a feedback loop using the collected degradation data. While the case study presented will primarily focus on a cable-driven APM gearbox, the underlying theory and the tools developed to undertake diagnostics and prognostics tasks are broadly applicable to a wide range of other civil and industrial applications.

Infrared images hold significant application value in fields such as military reconnaissance, security surveillance, and medical diagnosis. However, issues like low resolution, high noise, and complex degradation characteristics severely hinder their practical application effectiveness. This paper introduces an infrared super-resolution reconstruction algorithm based on a random degradation model and Generative Adversarial Networks (GANs), addressing the diversity of infrared image degradation processes. The primary contribution lies in explicitly modeling key degradation parameters of infrared images (such as blur kernel and noise distribution) using a random degradation model, generating diverse low-resolution to high-resolution image pairs, and significantly enhancing the model’s generalization ability for complex degradations. Experiments on the Airo infrared dataset and a self-built infrared dataset demonstrate that when the resolution is increased by a factor of 2, 4, and 8, the 4× resolution reconstructed images exhibit notable advantages in terms of texture detail and noise suppression. Especially in 4× super-resolution reconstruction, compared to three typical deep learning algorithms, our algorithm improves the Peak Signal-to-Noise Ratio (PSNR) by 3.046 dB, 1.8489 dB, and 0.2108 dB, respectively, and the Structural Similarity Index (SSIM) by 0.0387 (4.76%), 0.0287 (3.48%), and 0.0131 (1.56%), respectively, with perceptual similarity decreasing by 0.2465, 0.13344, and 0.0514 (lower values indicate better perceptual quality). Subjective visual assessments further validate the algorithm’s significant advantages in noise reduction and weak texture restoration. This study proposes an infrared image super-resolution reconstruction method based on random degradation modeling, which holds significant theoretical and practical value in complex infrared degradation scenarios.

Purpose For repairable systems (RSs), reliability estimation is generally performed using virtual age models. Virtual age models consider the effect of maintenance actions by reducing system age using restoration factor (RF). RF is generally estimated from system failure data using various statistical methods. However, RSs such as railway systems experience various types of maintenance actions at different times during their life cycle. To consider all these different types of actions, we need multiple RFs in the virtual age model. As failure data are limited, the estimation of so many parameters becomes a complex problem and it can lead to erroneous inferences. These RFs are representative of effects of maintenance activities on the system. Therefore, these can be predicted from the information about the maintenance actions performed on the system. The paper aims to discuss these issues. Design/methodology/approach The paper considers different types of maintenance actions to predict RF of the system. These maintenance actions involve the replacement of components at some level of assembly. Each component in an assembly has its own impact on assembly restoration. RF for assembly/systems can be obtained by aggregating effects of multiple component replacement using analytical hierarchy process . The RF values obtained for different types of maintenance actions are then used to calculate the virtual age of the system at different failure points. Using these virtual age failure points, suitable distribution is fitted and parameters are estimated. The distribution and parameters provide information about reliability of the system at any point of time. Findings This paper provides an easier approach that gives different RFs for different types of PM and CM. To calculate RFs, it considers the impact of maintenance actions performed as well as the impact of the component on which they are performed. It is simpler and gives more consistent results than other approaches, which estimate RF using different statistical methods. Originality/value This paper provides an alternative approach to predict RF parameters instead of estimating these parameters using statistical methods. Estimation of parameters using different statistical methods is complex in nature and gives erroneous and inconsistent results. The approach given in this paper is simpler and gives more reliable results. This approach can be useful in estimating parameters for RSs when failure data are limited.

In this paper, a method for the estimation of remaining useful lifetime (RUL) of lithium-ion batteries has been presented based on a combination of its capacity degradation and internal resistance growth models. The capacity degradation model is developed recently based on battery capacity test data. An empirical model for internal resistance growth is also developed based on electrochemical-impedance spectroscopy (EIS) test data. The obtained models are used in a particle filtering (PF) framework for making end-of-lifetime (EOL) predictions at various phases of its lifecycle. Further, the above two models were fused together to obtain a new degradation model for RUL estimation. It has been observed that the fused degradation model has improved the standard deviation of prediction as compared to the individual degradation models by maintaining satisfactory prediction accuracy. The effect of parameter variations on the performance of the PF algorithm has also been studied. Finally, the predictions are validated with experimental data. From the results it can be observed that with the availability of longer volume of data, the prediction accuracy gradually improves. The prognostics framework proposed in this paper provides a structured way for monitoring the state of health (SoH) of a battery.

With the continuous advancement of Industry 4.0 and intelligent manufacturing, remaining useful lifetime (RUL) prediction can forecast the future degradation state of machinery and then estimate the remaining service time before it loses its safe operation ability. Accordingly, a series of predictive maintenance strategies can be regulated in advance for equipment in the Industrial Internet of Things. To tackle the challenges of insufficiency of failure data and lack of confidence in RUL prediction results, a similarity-based relevance vector machine (SRVM) is proposed in this article. Primarily, the relationship among latent variables in the SRVM is learned adaptively through similarity computations to fully utilize the limited degradation data. Furthermore, these internal variables in the SRVM are treated as time-varying variables and re-estimated dynamically to provide RUL prediction with reliable confidence. The experiment results show that the prediction accuracy of the SRVM is higher than that of other baseline methods.

Data envelopment analysis (DEA) is an effective technique for measuring the efficiency of decision-making units (DMUs) with several inputs and various outputs. Traditional DEA requires crisp data. However, the data in real applications are often imprecise. In order to dominate this restriction, the fuzzy sets may be utilized with the classical DEA to permit expert to integrate ambiguous data into the model. However, fuzzy sets encounter the limitation of not considering the estimation of reliability of information. In view of this, Z-number has been extended to model fuzzy numbers with a degree of confidence. In this paper, we introduce a new DEA (abbreviated as Z-DEA) for working out CCR in which the input and/or output are Z-number variables. We do this task by converting the Z-DEA to classical fuzzy model on the base of a fuzzy expectation of the fuzzy sets. In our study, the expert utilizes the linguistic terms for expressing judgment and an estimation of reliability. To the best of our knowledge, compared with the traditional DEA frameworks, The DEA with Z-data can more practically handle real-world problems.

Advantages of new cardiovascular risk-assessment strategies in high-risk patients with hypertension

The trend towards intelligent control processes has introduced the Internet of Things (IoT) and cloud computing technologies to factories. IoT devices can sense data and send it to a cloud for further analysis in a factory. Consequently, the quantity of such valuable data flowing in an industrial cyber‐physical system has gradually increased. Tailoring a risk assessment system for Industrial IoT (IIoT) is essential, particularly for a cloud platform that handles the IIoT data flow. In this study, we leverage analytic hierarchy processes (AHPs) and propose Hierarchical Risk Assessment Model (HiRAM) for an IIoT cloud platform. The proposed model allows the platform to self‐evaluate its security status. Furthermore, a modular and responsive Risk Assessment System based on HiRAM, called HiRAM‐RAS, is realized and evaluated in a real‐world IIoT cloud platform. We deploy HiRAM‐RAS to a sample application and introduce the practical deployment procedures. We then estimate the practicality of the HiRAM‐RAS by injecting different degrees of errors and launching Distributed denial‐of‐service (DDoS) attacks. The results demonstrate the changes in integrity and availability scores evaluated by HiRAM.

Lifetime prognostics for power module bonding wires under non-constant stress conditions: A compound stochastic process degradation modeling approach