Vibrational Triboelectric Nanogenerator-Based Multinode Self-Powered Sensor Network for Machine Fault Detection

Physical parameter sensing largely benefits the lifetime and operational costs of machines and has been widely used for machine fault detection. Herein, in this article, we developed a multinode sensor network, which is fully self-powered by harvesting mechanical vibration energy, to establish a machine fault detection system. A multilayered vibrational triboelectric nanogenerator (V-TENG) was designed to scavenge energy from working machines. Triggered by a vibration motion with the frequency of 8 Hz, the V-TENG can generate an output with power density of 3.33 mW/m3. With a power management module, the microcontrol unit integrated with sensors and a wireless transmitter can be continuously powered by the V-TENG to construct a self-powered vibration sensor node (SVSN). A supporting vector machine algorithm-based machine fault detection system was then established through a three-SVSN network by acquiring acceleration and temperature data from the working machine. Based on the system, different working conditions of the machine were recognized with an accuracy of 83.6%. The TENG-based SVSN for machine fault detection has demonstrated wide prospects in production monitoring, intelligent manufacturing, and smart factory. Moreover, the proposed self-powered sensor network has great potential and wide application in the era of distributed Internet of Things, artificial intelligence, and big data.

[1]  Eric E. Ungar,et al.  Mechanical Vibration Analysis and Computation , 1989 .

[2]  Hua Yu,et al.  A Self‐Powered Dynamic Displacement Monitoring System Based on Triboelectric Accelerometer , 2017 .

[3]  P. Jacob,et al.  Surface electrostatic damage by microprocess robotic machines: diagnosis and reliability, process auditing, and remedies , 2006, IEEE Transactions on Device and Materials Reliability.

[4]  Long Jin,et al.  A linear-to-rotary hybrid nanogenerator for high-performance wearable biomechanical energy harvesting , 2020 .

[5]  Lorenzo Scalise,et al.  Fault detection for quality control of household appliances by non-invasive laser Doppler technique and likelihood classifier , 1999 .

[6]  Zhong Lin Wang,et al.  Reviving Vibration Energy Harvesting and Self-Powered Sensing by a Triboelectric Nanogenerator , 2017 .

[7]  Nannan Zhang,et al.  Micro-cable structured textile for simultaneously harvesting solar and mechanical energy , 2016, Nature Energy.

[8]  Bo-Suk Yang,et al.  Intelligent fault diagnosis of rotating machinery using infrared thermal image , 2012, Expert Syst. Appl..

[9]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[10]  Zhong Lin Wang,et al.  Triboelectric Nanogenerator Enabled Body Sensor Network for Self-Powered Human Heart-Rate Monitoring. , 2017, ACS nano.

[11]  Zhong Lin Wang,et al.  Triboelectric nanogenerator built inside shoe insole for harvesting walking energy , 2013 .

[12]  Chuxiong Hu,et al.  Torus structured triboelectric nanogenerator array for water wave energy harvesting , 2019, Nano Energy.

[13]  A. K. Wadhwani,et al.  Machine Fault Signature Analysis , 2008 .

[14]  Zhong Lin Wang,et al.  Integrated multilayered triboelectric nanogenerator for harvesting biomechanical energy from human motions. , 2013, ACS nano.

[15]  Long Lin,et al.  Grating‐Structured Freestanding Triboelectric‐Layer Nanogenerator for Harvesting Mechanical Energy at 85% Total Conversion Efficiency , 2014, Advanced materials.

[16]  Edwin Lughofer,et al.  Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations , 2014, Inf. Fusion.

[17]  Idriss El-Thalji,et al.  A summary of fault modelling and predictive health monitoring of rolling element bearings , 2015 .

[18]  Zhong Lin Wang,et al.  Power-generating shoe insole based on triboelectric nanogenerators for self-powered consumer electronics , 2013 .

[19]  Zhong Lin Wang,et al.  Flexible triboelectric generator , 2012 .

[20]  S. A. McInerny,et al.  Basic vibration signal processing for bearing fault detection , 2003, IEEE Trans. Educ..

[21]  Jun Chen,et al.  Harmonic‐Resonator‐Based Triboelectric Nanogenerator as a Sustainable Power Source and a Self‐Powered Active Vibration Sensor , 2013, Advanced materials.

[22]  Weiqing Yang,et al.  Harvesting broadband kinetic impact energy from mechanical triggering/vibration and water waves. , 2014, ACS nano.

[23]  Fengru Fan,et al.  Theoretical Comparison, Equivalent Transformation, and Conjunction Operations of Electromagnetic Induction Generator and Triboelectric Nanogenerator for Harvesting Mechanical Energy , 2014, Advanced materials.

[24]  Tao Jiang,et al.  Universal power management strategy for triboelectric nanogenerator , 2017 .

[25]  Sun Feng-rui.  Diagnostic Availability Research of Infrared Fault Diagnosis of Machine and Electric Device , 2004 .

[26]  Keren Dai,et al.  Harvesting Ambient Vibration Energy over a Wide Frequency Range for Self-Powered Electronics. , 2017, ACS nano.

[27]  Zhong Lin Wang,et al.  Remarkable merits of triboelectric nanogenerator than electromagnetic generator for harvesting small-amplitude mechanical energy , 2019, Nano Energy.

[28]  Zhongxiao Peng,et al.  Expert system development for vibration analysis in machine condition monitoring , 2008, Expert Syst. Appl..

[29]  V. K. Giri,et al.  Health Monitoring and Fault Diagnosis in Induction Motor-A Review , 2014 .

[30]  Sofie Van Hoecke,et al.  Thermal image based fault diagnosis for rotating machinery , 2015 .

[31]  Wei Tang,et al.  Rotating‐Disk‐Based Direct‐Current Triboelectric Nanogenerator , 2014 .

[32]  Weiqing Yang,et al.  Harvesting energy from the natural vibration of human walking. , 2013, ACS nano.

[33]  Tao Jiang,et al.  Multilayer wavy-structured robust triboelectric nanogenerator for harvesting water wave energy , 2016 .

[34]  Ruoyu Li,et al.  Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification , 2012, IEEE Transactions on Instrumentation and Measurement.

[35]  Zhong Lin Wang,et al.  Self-Powered Safety Helmet Based on Hybridized Nanogenerator for Emergency. , 2016, ACS nano.

[36]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[37]  Yu Song,et al.  High efficiency power management and charge boosting strategy for a triboelectric nanogenerator , 2017 .

[38]  Wei Zhou,et al.  Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control , 2008, IEEE Transactions on Industrial Electronics.

[39]  Sihong Wang,et al.  Freestanding Triboelectric‐Layer‐Based Nanogenerators for Harvesting Energy from a Moving Object or Human Motion in Contact and Non‐contact Modes , 2014, Advanced materials.

[40]  Tao Jiang,et al.  Triboelectric Nanogenerator Networks Integrated with Power Management Module for Water Wave Energy Harvesting , 2019, Advanced Functional Materials.

[41]  Swagatam Das,et al.  Multi-sensor data fusion using support vector machine for motor fault detection , 2012, Inf. Sci..

[42]  Zhouping Yin,et al.  Giant Voltage Enhancement via Triboelectric Charge Supplement Channel for Self-Powered Electroadhesion. , 2018, ACS nano.

[43]  Guang Zhu,et al.  Dipole-moment-induced effect on contact electrification for triboelectric nanogenerators , 2014, Nano Research.

[44]  Guoxu Liu,et al.  Self-powered intelligent buoy system by water wave energy for sustainable and autonomous wireless sensing and data transmission , 2019, Nano Energy.

[45]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .