A High-Accuracy Least-Time-Domain Mixture Features Machine-Fault Diagnosis Based on Wireless Sensor Network

Du, Cuicui; Gao, Shang; Jia, Naishu; Kong, Deren; Jian, Jiang; Tian, Gui Yun; Su, Yan; Wang, Qiaomu; Li, Chaoqun

doi:10.1109/jsyst.2020.2993337

Cited by 10 publications

(11 citation statements)

References 29 publications

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“…Frequency and time-frequency features also produce high-dimensional feature vectors, while time domain features are usually computationally efficient and suitable for edge feature extraction. Nine simple time domain parameters extracted at the edge have recently been investigated by [13] for wireless machine fault diagnosis. However, for SHM applications, the time domain features are sometimes less sensitive or robust to environmental and operational effects as compared to their frequency and time-frequency counterparts.…”

Section: Introductionmentioning

confidence: 99%

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Buckley

Ghosh

Pakrashi

2021

Sensors

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Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100–500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.

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Section: Introductionmentioning

confidence: 99%

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Buckley

Ghosh

Pakrashi

2021

Sensors

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“…s m,n ≤ c m,n : ð8Þ Equation (7) indicates that each node can simultaneously monitor F objects at most, and formula (8) represents that a target can be monitored by a sensor node only if it is within the node coverage.…”

Section: Target Coverage Modelmentioning

confidence: 99%

“…Subsequently, users can collect the information of interest or further control other nodes' actions on the network through the aggregation node. IWSN is usually deployed in harsh environments without infrastructure and unattended [8]. Due to their unique advantages of rapid deployment, survivability, high concealment, and low cost, IWSN is widely used in many fields.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

et al. 2021

Self Cite

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The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms.

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“…In addition, the sensor node can be deployed on a sealed space that a wired system is unable to set up. WSNs have recently been widely applied to monitor conditions such as coal mines [4], mechanical equipment [5], bridge condition monitoring [6], and wind power generation [7].…”

Section: Introductionmentioning

confidence: 99%

Path Perception Synchronization Trigger Method for Dual Mode Multiplexing Mechanical Vibration Wireless Sensor Networks

Hao¹,

Tang

Huang

et al. 2024

IEEE Access

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Synchronization triggers for mechanical-vibration wireless sensor networks play an important role in data acquisition and the analysis of multiple nodes. At present, most nodes are designed using a single protocol, which makes it difficult to achieve a balance between network establishment and synchronization. Meanwhile, such synchronization trigger methods rely on a clock, beacon, or communication between nodes. A method based on a clock and beacon requires frequent timestamps, which reduces the synchronization trigger accuracy and increases the power consumption. The communication between nodes requires these nodes to be at a close distance; otherwise, the node cannot receive synchronization information. Aiming at the above problems, a path perception synchronization trigger method based on dual-mode multiplexing and cluster networks is proposed. First, the node is designed using dual mode multiplexing. Second, an adjoint networking method is proposed to construct the cluster network topology. Next, a path perception time measurement method based on the terminal device and terminal assistant measurement without clock synchronization, beacon synchronization, and communication between nodes is discussed. Finally, a maximum time deviation compensation and time block synchronization trigger measurement method are proposed to solve the problems of a time delay compensation and system instruction interruption. The experimental results show that the maximum time error of the path perception synchronization trigger is between 50 and 60 μs, and the minimum time error is between 10 and 16 μs, which significantly improves the synchronization triggering accuracy under conditions that do not rely on clock synchronization, beacon synchronization, or communicating between nodes.

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A High-Accuracy Least-Time-Domain Mixture Features Machine-Fault Diagnosis Based on Wireless Sensor Network

Cited by 10 publications

References 29 publications

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Hybrid Niche Immune Genetic Algorithm for Fault Detection Coverage in Industry Wireless Sensor Network

Path Perception Synchronization Trigger Method for Dual Mode Multiplexing Mechanical Vibration Wireless Sensor Networks

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