A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring.

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100 Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168 000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

Recent Advances in Mechanical Vibration Energy Harvesters Based on Triboelectric Nanogenerators.

With the development of autonomous/smart technologies and the Internet of Things (IoT), tremendous wireless sensor nodes (WSNs) are of great importance to realize intelligent mechanical engineering, which is significant in the industrial and social fields. However, current power supply methods, cable and battery for instance, face challenges such as layout difficulties, high cost, short life, and environmental pollution. Meanwhile, vibration is ubiquitous in machinery, vehicles, structures, etc., but has been regarded as an unwanted by-product and wasted in most cases. Therefore, it is crucial to harvest mechanical vibration energy to achieve in situ power supply for these WSNs. As a recent energy conversion technology, triboelectric nanogenerator (TENG) is particularly good at harvesting such broadband, weak, and irregular mechanical energy, which provides a feasible scheme for the power supply of WSNs. In this review, recent achievements of mechanical vibration energy harvesting (VEH) related to mechanical engineering based on TENG are systematically reviewed from the perspective of contact-separation (C-S) and freestanding modes. Finally, existing challenges and forthcoming development orientation of the VEH based on TENG are discussed in depth, which will be conducive to the future development of intelligent mechanical engineering in the era of IoT.

Can adopting lean startup strategy promote the sustainable development of new ventures? The mediating role of organizational iterative learning.

When high uncertainty becomes the norm in entrepreneurship, entrepreneurial failure often becomes the first natural barrier that new ventures face. In dynamic environment, there is still a lack of clear answers on what strategic orientation new ventures need to adopt to improve organizational learning efficiency and achieve sustainable development. Lean startup theory believes that the entrepreneurial process is also a process where organizational cognition is constantly iterated and updated, which drives entrepreneurs to explore business opportunities through iteration learning and early customer insight. The paper aims to describe and examine the relationship between lean startup strategy, organizational iterative learning, and sustainable development of new ventures. This model is tested on the survey data of R&D departments of 325 technology new ventures in China. The research results show that: lean startup strategy can positively affect sustainable performance of new ventures; organizational iterative learning plays a mediating role in the relationship between lean startup strategy and sustainable development; market dynamics positively moderate the relationship between organizational iterative learning and sustainable development, while technology dynamics negatively moderate this relationship; furthermore, the two also moderate the process of lean startup strategy influencing sustainable development through organizational iterative learning, and that moderated mediating effect is significant. The research results reveal that entrepreneurs should deepen lean startup practices in new business layout, advocate the iteration and output of advanced knowledge, help enterprises establish proprietary knowledge barriers, and achieve sustainable development.

A Ring-Type Triboelectric Nanogenerator for Rotational Mechanical Energy Harvesting and Self-Powered Rotational Speed Sensing.

In recent years, sensors have been moving towards the era of intelligence, miniaturization and low power consumption, but the power-supply problem has always been a key issue restricting the popularization and development of machine-mounted sensors on the rotating machinery. Herein, we develop a ring-type triboelectric nanogenerator (R-TENG) that functions as a sustainable power source as well as a self-powered rotational speed sensor for rotating machinery. The R-TENG adopts a freestanding mode and consists of a ring-type container unit, an end cover and polytetrafluoroethylene (PTFE) cylinders. In this study, the influence of the number of cylinders, the PTFE cylinder's diameter and the rotational speed on the electrical output are systematically examined, and the motion law of the PTFE cylinders in the container is revealed by the experimental results and verified by kinetic simulation. At a rotational speed of 400 rpm, the output voltage, current and transferred charge of the designed R-TENG reached 138 V, 115 nC and 2.03 µA, respectively. This study provides an attractive power supply strategy for machine-mounted sensors of the rotating machinery, and the rotational speed measurement test also suggests the potential application of the R-TENG as a self-powered rotational speed sensor.

A Robust Silicone Rubber Strip-Based Triboelectric Nanogenerator for Vibration Energy Harvesting and Multi-Functional Self-Powered Sensing.

Vibration is a common phenomenon in various fields which can not only indicate the working condition of the installation, but also serve as an energy source if it is efficiently harvested. In this work, a robust silicone rubber strip-based triboelectric nanogenerator (SRS-TENG) for vibration energy harvesting and multi-functional self-powered sensing is proposed and systematically investigated. The SRS-TENG consists of a silicone rubber strip and two aluminum electrode layers supported by polylactic acid (PLA), and acts as a sustainable power source and vibration frequency, amplitude and acceleration sensor as well. The soft contact between the aluminum electrode and silicone rubber strip makes it robust and stable even after 14 days. It can be applied in ranges of vibration frequencies from 5 to 90 Hz, and amplitudes from 0.5 to 9 mm, which shows it has advantages in broadband vibration. Additionally, it can achieve lower startup limits due to its soft structure and being able to work in multi-mode. The output power density of the SRS-TENG can reach 94.95 W/m3, matching a resistance of 250 MΩ, and it can light up more than 100 LEDs and power a commercial temperature sensor after charging capacitors. In addition, the vibration amplitude can be successfully detected and displayed on a human-machine interface. Moreover, the frequency beyond a specific limit can be distinguished by the SRS-TENG as well. Therefore, the SRS-TENG can be utilized as an in situ power source for distributed sensor nodes and a multifunctional self-powered vibration sensor in many scenarios.

A High-Performance Flag-Type Triboelectric Nanogenerator for Scavenging Wind Energy toward Self-Powered IoTs.

Pervasive and continuous energy solutions are highly desired in the era of the Internet of Things for powering wide-range distributed devices/sensors. Wind energy has been widely regarded as an ideal energy source for distributed devices/sensors due to the advantages of being sustainable and renewable. Herein, we propose a high-performance flag-type triboelectric nanogenerator (HF-TENG) to efficiently harvest widely distributed and highly available wind energy. The HF-TENG is composed of one piece of polytetrafluoroethylene (PTFE) membrane and two carbon-coated polyethylene terephthalate (PET) membranes with their edges sealed up. Two ingenious internal-structure designs significantly improve the output performance. One is to place the supporting sponge strips between the PTFE and the carbon electrodes, and the other is to divide the PTFE into multiple pieces to obtain a multi-degree of freedom. Both methods can improve the degree of contact and separation between the two triboelectric materials while working. When the pair number of supporting sponge strips is two and the degree of freedom is five, the maximum voltage and current of HF-TENG can reach 78 V and 7.5 µA, respectively, which are both four times that of the untreated flag-type TENG. Additionally, the HF-TENG was demonstrated to power the LEDs, capacitors, and temperature sensors. The reported HF-TENG significantly promotes the utilization of the ambient wind energy and sheds some light on providing a pervasive and sustainable energy solution to the distributed devices/sensors in the era of the Internet of Things.

Adaptive baseline model for autonomous marine equipment and systems.

With the rapid development of the Internet of Things (IoT) and the Fourth Industrial Revolution, marine equipment and systems are becoming increasingly automated and autonomous. Judging the status of equipment and systems for autonomous shipping assumes that the benchmark of status evaluation is not easily obtained, and the performance baseline for the benchmark is usually static and cannot be accurately adapted under dynamic operating conditions. This paper deals with the issue of establishing a baseline for marine equipment and systems by using a data-driven method. Considering the working conditions of marine equipment and systems, a reference-site (R-S) model was first proposed to determine the initial baseline. This method could solve the problem of inadequate parameters in the initial state very well. Then, a dynamic kernel (D-K) model was used to increase the number of reference sites and update the reference points. This method reduced the amount of data calculation in the process of a dynamic update of the baseline. Continuously fitting the reference points enabled the dynamically updated performance baseline to cope with the working conditions. To implement the proposed method, the index parameters in the R-S and D-K models were processed, and the sliding window capacity was determined using the Kolmogorov-Smirnov method. Finally, the proposed baseline model was applied to a practical case of the exhaust temperature of a marine diesel engine. The result revealed that the proposed method yielded a more accurate baseline and better adaptive performance.

A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring.

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

[MOA spectral analysis of additive element contents in lube oil].

The determination of the additive contents of marine lubrication oil by using atomic emission spectrometry is described. A new measuring method is suggested in accordance with the working principle of MOA atomic emission spectral instrument. The additive element contents, if not within the precision limits of MOA atomic emission spectra, are to be measured by being diluted with a standard oil. Error analysis is conducted and the calibration curve method is used. The testing result indicates that the RSD of Ca, P and Zn is 1.6%, 4.8% and 4.6% respectively, and the error result of oil sample before and after diluting is 4.21%, 6.99% and 5.09% respectively.

[Study on determination of sodium, vanadium and aluminum contents in fuel oil by ICP-AES].

The present paper considers synthetically all kinds of factors affecting excitation spectrum under traditional measurement conditions of fluorescence matter in liquor using plasma atomic emission spectra. The input power, carrier gas flow and assistant gas flow for BEC of Na, V, Al were optimized by testing. The input power, carrier gas flow and assistant gas flow for Na are 950 W, 0.6 L x min(-1) and 1.0 L x min(-1), respectively. The input power, carrier gas flow and assistant gas flow for V are 1 150 W, 0.5 and 1.1 L x min(-1), respectively. The input power, carrier gas flow and assistant gas flow for Al are 1 150 W, 0.6 and 1.0 L x min(-1), respectively. The result shows that the method is sensitive, accurate, linear in a wide range and highly precise. The precision is between 1.7%-2.2%, the linear ranges are between 0-100 mg x L(-1) and recoveries are between 96%-105%.

[Study on the characteristic parameter of oil spectrometric analysis].

This paper establishes a new mathematics model of fault diagnosis basis of oil spectrometric analysis by means of long time following up the mechanical equipment's lubrication oil. The characteristic parameter of oil spectrometric analysis has been confirmed using a new way that the laboratory experiment study and the mechanical equipment's lubrication oil spectrometric analysis are combined in order to confirm the time of fault and avoid serious fault. It will provide the academic and actual basis for RCM and enhance the dependability. The result of examples proves that the characteristic parameter of oil spectrometric analysis has very high stability and veracity. This method has been proved that it was effective in fault diagnosis basis of oil spectrometric analysis.