Crack detection in rotating shafts using wavelet analysis, Shannon entropy and multi-class SVM

Incipient fault diagnosis is essential to detect potential abnormalities and failures in industrial processes which contributes to the implementation of fault-tolerant operations for minimizing performance degradation. In this paper, an innovative method named Self-adaptive Entropy Wavelet (SEW) is proposed to detect incipient transverse crack faults on rotating shafts. Continuous Wavelet Transform (CWT) is applied to obtain optimized wavelet function using impulse modelling and decompose a signal into multi-scale wavelet coe?cients. Dominant features are then extracted from those vectors using Shannon entropy, which can be used to discriminate fault information in di?erent conditions of shafts. Support Vector Machine (SVM) is carried out to classify fault categories which identi?es the severity of crack faults. After that, the effectiveness of this proposed approach is investigated in testing phrase by checking the consistency between testing samples with obtained model, the result of which has proved that this proposed approach can be effectively adopted for fault diagnosis of the occurrence of incipient crack failures on shafts in rotating machinery.</p

Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.</p

Data collection middleware for crowdsourcing-based industrial sensing intelligence

In this paper, crowdsourcing-based industrial sensing intelligence (CISI) is proposed as a collaborative approach for large-scale monitoring in modern industrial plants, targeting at improved productivity and increased workplace safety. The proposed approach focuses on middleware, which considers both application and industry-grade requirements. Through embedding crowdsourcing knowledge at different levels and supporting QoS services, systems based on CISI can perform effective work assignment and flexible configuration of wireless sensor networks (WSNs). This paper presents a middleware that addresses these characteristics, which is an extension of GSN, our earlier work on middleware for rapid deployment and integration of heterogeneous sensor networks. Wireless sensor devices and wearable equipment are employed as modeling tools for the middleware implementation.</p

Using wearable equipment to construct monitoring maps in large-scale petrochemical plants

This paper focuses on using wearable equipment to make workers participate in constructing monitoring maps in large-scale petrochemical plants, which collaborates with static sensor nodes in given areas. Furthermore, this study provides a sensing pattern with less cost and higher flexibility, which effectively contribute to collective effort with static sensor nodes. Several open research issues are discussed.</p

A review of research on acoustic detection of heat exchanger tube

Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube.</p

Internet of Things for noise mapping in smart cities: state of the art and future directions

Noise pollution has been an issue since ancient times. Recently, this problem has been exacerbated due to rapid population growth and urbanization. Noise mapping is a strategic action plan that visualizes the long-term and real-time noise pollution of our cities, industrial sites, and other regions of interest. This article first discusses the working principle of general model-based noise mapping and the lessons learned. Then, in-depth descriptions of the technical challenges and design issues of noise mapping using mobile crowdsensing and acoustic sensor networks are presented. Finally, we provide our insights for future research directions regarding artificial intelligence assisted noise prediction, constructive interference for multimedia transmission, and simultaneous noise sensing and sound energy harvesting as well as inaudible sound attacks and defense

On enabling mobile crowd sensing for data collection in smart agriculture: a vision

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem

On enabling mobile crowd sensing for data collection in smart agriculture: a vision

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges that are required to be considered. Mobile Crowd Sensing (MCS) embeds three beneficial characteristics: a) cost-effectiveness, b) scalability, and c) mobility and robustness. With the Internet of Things (IoT) becoming a reality, the smart phones are widely becoming available even in remote areas. Hence, both the MCSs characteristics and the plug and play widely available infrastructure provides huge opportunities for the MCS-enabled smart agriculture.opening up several new opportunities at the application level. In this paper, we extensively evaluate the Agriculture Mobile Crowd Sensing(AMCS) and provide insights for agricultural data collection schemes. In addition, we provide a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low cost requirements. However, we note that AMCSs may still posses limitations in regard to data integrity and quality to be considered as a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns the MCS-enabled agriculture by putting forward six potential applications of AMCS-enabled agriculture. Finally, we propose future research and focus on agricultural characteristics, e.g., seasonality and regionality.</p