Predictive Maintenance Framework Research Articles

Enhancing Industrial Process Control: Integrating Intelligent Digital Twin Technology with Proportional-Integral-Derivative Regulators

This paper explores the integration of intelligent digital twin technology with PID regulators in industrial process control utilizing smart meter data. It presents a novel approach involving the creation of mathematical models to simulate real-time system behavior, thereby enhancing the PID control loop. The focus is on the development of specialized IT infrastructure to support this integration, which includes data acquisition, processing, and control optimization. This integration aims to not only improve control system efficiency but also introduce a robust predictive maintenance framework, offering significant benefits across a wide range of industrial applications.

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT).

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

Enhancing Predictive Maintenance in Water Treatment Plants through Sparse Autoencoder Based Anomaly Detection

The deployment of Machine Learning (ML) for improving Water Treatment Plants (WTPs) predictive maintenance is investigated in the present article. Proactively detecting and fixing functional difficulties which might cause catastrophic effects has historically been an endeavour for reactive or schedule-based maintenance methods. Anomaly Detection (AD) in WTP predictive maintenance frameworks is the primary goal of this investigation, which recommends a novel approach based on autoencoder (AE)-based ML models. For the objective of examining high-dimensional time-series sensor data collected from a WTP over a long time, Sparse Autoencoders (SAEs) are implemented. The data collected involves an array of operational measurements that, evaluated together, describe the plant's overall performance. With the support of the AE, this work aims to develop a practical framework for WTP operation predictive maintenance. Anomalies are all system findings from testing that might result in flaws or malfunctions. The research article analyses January and July 2023 WTP data from Jiangsu Province China. The AE paradigm had been evaluated using F1-scores, recall, accuracy, and precision. SAE has substantially improved AD functionality.

A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation

To support the mission of providing safe electricity generation with a high capacity factor, a Predictive Maintenance (PdM) framework using Machine Learning Models (MLM) to optimize component maintenance operations is developed. Using sensor measurements to better predict the true component’s Remaining Useful Life (RUL), the PdM framework has the potential to optimize maintenance costs by performing maintenance only when necessary. The PdM framework to pump bearings, the framework consists of a Convolutional Neural Network Autoencoder (CNN-AE) to detect component deviations from normality, a CNN to characterize component fault modes, and a Bayesian Neural Network (BNN) to estimate the component RUL with uncertainty. To increase the number of training samples, a synthetic data generation procedure was developed and includes procedures to recreate the fault-specific characteristic frequencies for diagnostics and the degradation trends for prognostics. The MLMs trained on the synthetic data are tested on the Center for Intelligent Maintenance Systems (IMS) dataset to showcase how well the synthetic data replicates measurement data. Utilizing this framework, the PdM was found to delay maintenance on average by a total of 8.92 years over 40 years and decrease the unexpected component failure rate from 10% to 0% when compared to traditional maintenance philosophies.

Predictive Maintenance Framework for Fault Detection in Remote Terminal Units

The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory.

An Innovative Decentralized and Distributed Deep Learning Framework for Predictive Maintenance in the Industrial Internet of Things

An Innovative Decentralized and Distributed Deep Learning Framework for Predictive Maintenance in the Industrial Internet of Things

PreM-FedIoV: A Novel Federated Reinforcement Learning Framework for Predictive Maintenance in IoV

PreM-FedIoV: A Novel Federated Reinforcement Learning Framework for Predictive Maintenance in IoV

A review on Machine Learning Enhanced Predictive Maintenance for Electric Vehicle Power Electronics: A Pathway to Improved Reliability and Longevity

The emergence of electric vehicles (EVs) as a mainstream mode of transportation presents new challenges in the realm of power electronics, particularly concerning reliability and longevity. Power electronics are the cornerstone of EV performance, dictating efficiency, durability, and overall vehicle health. Traditional maintenance strategies fall short in addressing the dynamic operational demands and complex failure mechanisms inherent in EV power systems. This paper introduces a machine learning (ML)-enhanced predictive maintenance framework designed to revolutionize the upkeep of EV power electronics. By harnessing advanced ML algorithms, the framework predicts potential system failures and degradation patterns, enabling preemptive maintenance actions. A robust data-driven approach is employed, utilizing operational data and failure modes to train the predictive models. The efficacy of the proposed method is demonstrated through extensive simulation and real-world EV power system analyses, showcasing significant improvements in fault identification accuracy and maintenance scheduling optimization. The result is a substantial extension of component lifespan and a reduction in unplanned downtimes, propelling EV power electronics towards higher reliability standards. This work not only contributes a novel predictive maintenance methodology but also paves the way for adaptive maintenance regimes, tailored to the unique demands of EV power electronics systems in the pursuit of sustainable and resilient transportation solutions.

Predictive Maintenance in IoT Devices using Time Series Analysis and Deep Learning

The pervasive integration of Internet of Things (IoT) devices across industries has ushered in a new era of data-driven operational efficiency. However, the reliability and uninterrupted functionality of these interconnected devices necessitate innovative approaches to maintenance. This research focuses on the development and implementation of a predictive maintenance framework for IoT devices, leveraging the synergies between Time Series Analysis (TSA) and Deep Learning (DL) techniques. The primary objective of this study is to enhance the accuracy and efficiency of predictive maintenance processes, ultimately minimizing downtime and optimizing resource utilization. The research methodology involves the collection of diverse data types from IoT devices, encompassing sensor readings, error logs, and historical maintenance records. A meticulous data preprocessing stage follows, involving cleaning, normalization, and feature extraction to prepare the dataset for analysis. The core analytical components of the proposed framework include Time Series Analysis for uncovering temporal patterns in the IoT data. Statistical methods and time series decomposition are applied to identify trends and seasonality, providing valuable insights into the device's performance over time. Concurrently, Deep Learning models, specifically recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), are employed to predict maintenance needs based on historical patterns. Results obtained from the application of the predictive maintenance framework to real-world IoT datasets demonstrate promising accuracy and efficiency in anticipating maintenance requirements. The paper identifies existing challenges in predictive maintenance for IoT devices and suggests future research directions. These include the exploration of edge computing, federated learning, and the integration of explainable AI to enhance model interpretability. In conclusion, the study underscores the significance of predictive maintenance in ensuring the reliability of IoT devices, offering a roadmap for industries seeking to harness the full potential of data analytics and artificial intelligence for operational excellence.

Digital twin-enhanced predictive maintenance for indoor climate: A parallel LSTM-autoencoder failure prediction approach

Recently, the emergency of predictive maintenance (PdM) in the building industry has expanded from facilities to indoor climates, as air quality is highly relevant to residential health, comfort, and work efficiency. Besides, digital twin (DT) is considered as an effective solution for PdM deployment because it significantly enhances data-driven insights and enables proactive interventions. However, most existing studies on indoor climate focus on condition monitoring or anomaly detection rather than failure prediction, which has higher requirements for data and algorithms. In this study, the remaining useful life (RUL) and time shift (TS) methods are employed to split the prediction problem into the combination of a supervised and an unsupervised subtask, followed by the development of a parallel prediction model integrating the long short-term memory network (LSTM) and autoencoder (AE) methods. Besides, a DT-enabled PdM framework has been proposed for indoor climates, validated through the establishment of an online platform designed to reconstruct the 3D building model and demonstrate real-time monitoring and alert information of indoor climates. Experiments show the effectiveness of the proposed model under different warning times and fault severity through a comparison study with other 4 benchmark models based on a practical dataset collected from different buildings in Singapore, while the practical online platform serves as an illustrative case for future DT-enhanced PdM solutions.

Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance

Deep reinforcement learning holds significant potential for application in industrial decision-making, offering a promising alternative to traditional physical models. However, its black-box learning approach presents challenges for real-world and safety-critical systems, as it lacks interpretability and explanations for the derived actions. Moreover, a key research question in deep reinforcement learning is how to focus policy learning on critical decisions within sparse domains. This paper introduces a novel approach that combines probabilistic modeling and reinforcement learning, providing interpretability and addressing these challenges in the context of safety-critical predictive maintenance. The methodology is activated in specific situations identified through the input–output hidden Markov model, such as critical conditions or near-failure scenarios. To mitigate the challenges associated with deep reinforcement learning in safety-critical predictive maintenance, the approach is initialized with a baseline policy using behavioral cloning, requiring minimal interactions with the environment. The effectiveness of this framework is demonstrated through a case study on predictive maintenance for turbofan engines, outperforming previous approaches and baselines, while also providing the added benefit of interpretability. Importantly, while the framework is applied to a specific use case, this paper aims to present a general methodology that can be applied to diverse predictive maintenance applications.

A Fine-grained Semi-supervised Anomaly Detection Framework for Predictive Maintenance of Industrial Assets

Reliable operation of industrial assets is of high priority for businesses where productivity determines the ability to deliver safety-critical products of high quality in a timely manner. The aerospace industry leads the demand for predictive maintenance (PdM). In the manufacturing space, unscheduled down time causes production delay and increases operational costs while introducing potential risks in product quality and on-time delivery. In field application of these products, unexpected breakdown of critical components can result in safety-critical events. Failure events are, therefore, extremely rare in industrial settings. Diverse operating conditions in the manufacturing environment and field applications contribute to the heterogeneous nature of data collected from these assets. This work presents an anomaly detection framework for PdM of industrial assets to address the practical challenges of scarce failure data sources and heterogeneous data across assets. We introduce a fine-grained modeling approach that efficiently accounts for individual asset differences in a semi-supervised fashion which requires only normal operation data for model training. The framework is demonstrated with an industrial 4.0 use case. Vibration sensor data from pumps in one of our manufacturing facilities is ingested to enable PdM with 2 weeks lead time using the proposed framework. This transforms unexpected breakdown time to scheduled maintenance, thereby reducing cost of delays and operation interruptions. The systematic implementation of the framework in the case study covers the practical production aspects including data quality evaluation, model training, optimization and daily serving of predictions. Furthermore, implementation challenges and recommendations are discussed based on the end-to-end solution implementation experiences.

Degradation modeling and opportunistic maintenance for two-component systems with an intermittent operation component

In this study, an opportunistic predictive maintenance framework was developed for a system comprising one continuous operation component (COC) and intermittent operation component (IOC). The COC operates consistently with the system. In contrast, the IOC operates alternately in operation and standby modes. The degradation of the COC can increase the working frequency of the IOC, which can accelerate its degradation process. Omitting this degradation interaction in degradation modeling and reliability assessment could result in inaccurate lifetime prognostic and suboptimal maintenance plans. Therefore, in this study, a degradation model for the IOC is proposed considering the influence of the degradation state of the COC. A reliability model for the IOC was derived and used for predictive maintenance. Based on the proposed degradation models, an opportunistic predictive maintenance policy was proposed, considering the structural and economic dependencies between the components. Finally, a case study of an air compressor system is conducted to demonstrate the viability and benefits of the proposed model.

Multivariate Time-Series Classification of Critical Events from Industrial Drying Hopper Operations: A Deep Learning Approach

In recent years, the advancement of Industry 4.0 and smart manufacturing has made a large amount of industrial process data attainable with the use of sensors installed on machines. This paper proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper, which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms to classify Multivariate Time-Series (MTS) data into two categories—failure or unusual events and regular events—thus formulating the problem as a binary classification. The raw data extracted from the sensors contained missing values, suffered from imbalancedness, and were not labeled. Therefore, necessary preprocessing is performed to make them usable for DL algorithms and the dataset is self-labeled after defining the two categories precisely. To tackle the imbalanced data issue, data balancing techniques like ensemble learning with undersampling and Synthetic Minority Oversampling Technique (SMOTE) are used. Moreover, along with DL algorithms like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), Machine Learning (ML) algorithms like Support Vector Machine (SVM) and K-nearest neighbor (KNN) have also been used to perform a comparative analysis on the results obtained from these algorithms. The result shows that CNN is arguably the best algorithm for classifying this dataset into two categories and outperforms other traditional approaches as well as deep learning algorithms.

Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost

Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost

Assessment framework for asset maintenance in passenger terminals

Asset management is an emerging discipline worldwide since most organisations are facing challenges in sustaining their operations with the ageing of assets. Moreover, managing of assets and the cost of maintenance are real-time challenges for organisations. Hence, the objective of this study is to develop a framework for predictive maintenance of assets considering the existing assessment frameworks of early researchers. The significance of this research is that the framework was developed focusing on passenger terminals where this concept had been narrowly addressed. The Kandy Multimodal Transit Terminal was selected as the case study. Accordingly, a Markov chain was used for developing the framework. A conceptual framework was introduced for the predictive maintenance of assets and cost projections considering the availability of data. The outcome of this research facilitates reduction of the consequences in asset maintenance management and management of the budget allocation for the maintenance in passenger terminals.

Data-driven predictive maintenance framework for railway systems

The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system.

A prognostic driven predictive maintenance framework based on Bayesian deep learning

Recent years have witnessed prominent advances in predictive maintenance (PdM) for complex industrial systems. However, the existing PdM literature predominately separates two inter-related stages—prognostics and maintenance decision making—and either studies remaining useful life (RUL) prognostics without considering maintenance issues or optimizes maintenance plans based on given/assumed prognostic information. In this paper, we propose a prognostic driven dynamic PdM framework by integrating the two stages. In the prognostic stage, we characterize the latent structure between degradation features and RULs through a Bayesian deep learning model. By doing so, the framework is capable of generating a predictive RUL distribution that can well describe prognostic uncertainties. In the maintenance decision-making stage, we dynamically update maintenance and spare-part ordering decisions with the latest predictive RUL information, while satisfying operational constraints. The advantage of the proposed PdM framework is validated by comparison with several benchmark polices, based on the famous C-MAPSS turbofan engine data set.

Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines

The increasing availability of condition-monitoring data for components/systems has incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the past years. However, most studies focus on point RUL prognostics, with limited insights into the uncertainty associated with these estimates. This limits the applicability of such RUL prognostics to maintenance planning, which is per definition a stochastic problem. In this paper, we therefore develop probabilistic RUL prognostics using Convolutional Neural Networks. These prognostics are further integrated into maintenance planning, both for single and multiple components. We illustrate our approach for aircraft turbofan engines. The results show that the optimal replacement time for the engines is close to the lower bound of the 99% confidence interval of the RUL estimates. We also show that our proposed maintenance approach leads to a cost reduction of 53% compared to a traditional Time-based maintenance strategy. Moreover, compared with the ideal case when the true RUL is known in advance (perfect RUL prognostics), our approach leads to a limited number of failures. Overall, this paper proposes an end-to-end framework for data-driven predictive maintenance for multiple components, and showcases the potential benefits of data-driven predictive maintenance on cost and reliability.

A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things

The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources.