Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0

Compare, Michele; Baraldi, Piero; Zio, Enrico

doi:10.1109/jiot.2019.2957029

Cited by 154 publications

(65 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The prime consideration is the development of advanced national and international standards regulating the current and futuristic requirement of the industry. Moreover, the implementation of suitable measurement methods and processes conforming to relevant national and international standards along with selection of appropriate equipment is the necessity of Industry 4.0 [39].…”

Section: Implications Of Industry 40 and Digitalizationmentioning

confidence: 99%

“…It is considered that the acquisition and storing of large amount of data in cloud can provide better performance of industry. But the storage of unnecessary data in cloud demands for more storage; hence, the cost increases nulling the benefit of digitalization [51]. The raw data should be filtered so that only required data are stored in the cloud.…”

Section: Edge Computing In Industry 40mentioning

confidence: 99%

See 1 more Smart Citation

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Varshney

Garg

Nagla

et al. 2021

MAPAN

View full text Add to dashboard Cite

The current advances and innovations in sensor technologies attributed to Industry 4.0 serve as the backbone for the inclusive growth of industry and ramping up the economy of any country. Industry 4.0 was basically conceptualized by introducing the Internet of Things (IoT) and Information and Communication technologies (ICT) that serve as an interface between digital and physical world through the fusion of smart sensors. The role of smart sensors and IoT-enabled industrial infrastructure is pivotal for adapting to the advanced technologies based on fusion of smart sensors. Digital meteorological traceability and uses of intelligent sensors, instrumentation and machinery in Industry 4.0, Smart Cities, Digital India and AtmaNirbhar Bharat missions of the government of India, are not only highly important but also in huge demand, which is going to increase manifolds in the years to come. The present paper is an attempt to provide a terse review and perspectives related to the advanced technological developments in this field and the challenges therein.

show abstract

Section: Implications Of Industry 40 and Digitalizationmentioning

confidence: 99%

Section: Edge Computing In Industry 40mentioning

confidence: 99%

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Varshney

Garg

Nagla

et al. 2021

MAPAN

View full text Add to dashboard Cite

show abstract

“…During this era of Industry 4.0, big data analytics, deep learning using offline and online streams of data are effectively utilized for fault diagnosis. FDD will improve and contribute better toward improving availability of the complex systems and revenues to the industry (Compare, 2019; 207 Bhatter et al, 2020 208 ).…”

Section: Research Analysis and Potential Research Directionmentioning

confidence: 99%

Reliability‐based fault analysis models with industrial applications: A systematic literature review

Ahmed

Raza

Al-Anazi

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets‐intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state‐of‐the‐art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer‐reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators. A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co‐citation network analysis. Later, a detailed content analysis of the top‐100 most‐cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability‐based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset‐intensive industry, researchers, and policymakers.

show abstract

“…These developments have resulted in companies with networked assets producing a massive amount of data [2]. For useful data provision, cloud-based solutions are required [10,11], while the IoT is the key enabling technology for machine-to-machine interaction [3]. Communication between hardware, software, and other devices is usually described under the term "connectivity".…”

Section: Cyber-physical Production Systemsmentioning

confidence: 99%

“…In the context of Industry 4.0, predictive maintenance (PdM) strongly relies on the Internet of Things (IoT) for timely data exchange. IoT-enabled PdM is attracting considerable investment from industries nowadays [3].…”

Section: Introductionmentioning

confidence: 99%

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

2020

View full text Add to dashboard Cite

The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations.

show abstract

Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0

Cited by 154 publications

References 114 publications

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Challenges in Sensors Technology for Industry 4.0 for Futuristic Metrological Applications

Reliability‐based fault analysis models with industrial applications: A systematic literature review

Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill

Contact Info

Product

Resources

About