The long-term intelligent machine condition monitoring system is essential in improving maintenance costs and decision-making. Triboelectric nanogenerator (TENG) has a great advantage in developing self-powered machine condition monitoring. The main issues preventing TENG for such applications are poor integration to machine components, limited operational range, and weak durability. In this work, durable non-contact TENG energy harvester adaptive is designed to mechanical shafts for harvesting rotational energy. The harvester is designed to automatically switch to the non-contact mode by using centrifugal force to avoid manual switching while operating over a wide range of speeds of 0–2000 rpm. The designed TENG generates a high output of up to 25 mW with excellent stability for >20 days of continuous operation and exhibits a high-power density of 286 W m−3. Moreover, a self-powered long-term continuous condition monitoring system is developed from a TENG sensor, energy harvester, and wireless module. The developed system successfully sends possible machine fault frequency every 74 min to the cloud and accessible anywhere. This is the only TENG design reported in the literature that can fully power a Wi-Fi module to send data. Hence, the result promises the practical application of the system in developing internet of things (IoT) in the Industry.

Hydropower is important in the modern power system due to its ability to quickly adjust production. More frequent use of this ability may lead to increased maintenance needs, highlighting the importance of research in condition monitoring for hydropower. This study suggests a model approach for friction monitoring of the bearings inside the Kaplan turbine’s hub. The approach is developed for when normal and anomalous data exist. The study compares isolation forest (iForest), local outlier factor (LOF), one-class support vector machine (OC-SVM), and Mahalanobis distance (MD) for anomaly detection, where iForest and OC-SVM appear to be good choices due to their robust performance. A moving decision filter (MDF) is fed with the output from the anomaly detection models to classify the data as normal or anomalous. The parameters in the MDF are optimized with Bayesian optimization to increase the performance of the models. The approach is tested using data from two actual hydropower turbines. The study shows that the model approach works for both turbines. However, the parameter optimization must be performed separately for each turbine.

One of the key aspects for correct lubricant operation in industry is the control of contamination levels, given that contaminants can lead to increased wear in mechanical systems and accelerated degradation of lubricants. Most of the available filtration techniques focus on removing particles above 4 µm, but not much information is available on the risks of submicron contaminants for lubricant degradation. In this work, a qualitative analysis of the recovered contaminants from a commercial lubricant sample was performed after being treated with different depth filtration methods. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) were used to observe the contaminants and their composition. Results suggest that conventional depth-filtration systems are not enough to remove submicron contaminants. When combined with other technologies, such as the chemo-mechanical depth filtration system used in this study, the removal of submicron contaminants is enhanced, as seen by the SEM segmentation of the particles present on the analyzed surfaces. Additionally, it is seen that the composition of the contaminant particles is a mixture of wear metals and common additive elements that react and form different oxides and phosphates, which suggest that the interaction between the metal wear particles and the lubricant can have a potential negative impact on the lubricant’s additivation, hence reducing its functionality and service life. 

Unwanted ice build-up is a ubiquitous phenomenon in nature, which creates a series of catastrophic impacts on a wide range of human activities. Various anti/de-icing materials have been proposed for dealing with icing issues. Superhydrophobic anti/de-icing coatings have been widely reported since it has high efficiency and can be achieved in different ways. The surface functional groups have a significant influence on surface energy which is related to surface wettability. However, the influence of the coating surfaces functional groups on the anti-/de-icing properties is still not well studied. To investigate this influence, different groups with different hydrophilicity have been introduced to 3,3,4,4,5,5,6,6,7,7,8,8,8-Tridecafluorooctyl acrylate (TFOA) to fabricate several branch copolymer ice-phobic coatings. The anti-icing performance and the influence of group radius and interaction were studied. The acrylic acid TFOA showed a great superhydrophobic property (over 150° water contact angle), lower ice adhesion strength (<50 kPa), and lower wear depth compared with other copolymer coatings. The mechanism was studied via the molecular dynamic calculation carried out in ChemDraw software. The interaction between hydrophobic and hydrophilic groups and the steric length of the hydrophilic groups influence the surface structure and surface element distribution, further influencing the ice adhesion strength.

Intelligent machine condition monitoring is desirable to enable Industry 4.0 and 5.0 to create sustainable products and services via the integration of automation, data exchange, and human–machine interface. In the past decades, huge progress has been achieved in establishing sustainable machine condition monitoring systems via various sensing technologies. Yet, the dependence on external power sources or batteries for sensing and data communication remains a challenge. In addition, energy harvesting and sensing are dynamically growing research fields introducing various working mechanisms and designs for improved performance, flexibility, and integrability. Recently, triboelectric nanogenerators (TENG) have been applied as a new technology for energy harvesting and sensing to monitor machine performance. This manuscript presents the potential application of TENG for self-powered sensors and energy harvesting technology for machine condition monitoring, where the developmental aspects of TENG-based devices including the robustness of design and device integration to machine elements are reviewed. For better comparison, the performance of various reported devices is summarized. Simultaneously, the advanced results achieved in employing TENGs for various condition analysis techniques and self-powered wireless communication for machine condition monitoring are discussed. Finally, the challenges, and key strategies for utilizing TENGs for machine condition monitoring in the future, are presented.

Wind power is considered as a sustainable and environmentally friendly energy source. However, the occurrence of icing poses significant challenges to energy production, particularly in frigid regions during the winter season. Conventional strategies employed for preventing and removing ice formation have proven inadequate due to their inability to satisfy intricate requirements or their high energy consumption. In this study, a commercial gelcoat coating was adopted as an anti-/de-icing coating by introducing different concentrations of graphene and boron nitride into the gelcoat coating through physical mixing. Extensive investigations were conducted on the correlation between anti-/de-icing, wear resistance, and thermal conductivity. Notably, the incorporation of nanoparticles induced a rise in the surface roughness, resulting in prolonged resistance to water icing on the coated surface. The wear resistance and thermal conductivity of the composite coating were enhanced through the inclusion of boron nitride and graphene. The building of thermal conductive particle networks improved thermal conductivity which can lead to improved heat transfer and heat distribution. At the same time, the enhanced gelcoat composite coating exhibited exceptional passive anti-/de-icing performance and wear resistance. This coating can replace commercial coatings to improve anti-/de-icing efficiency for the existing active heating anti-/de-icing techniques available in the market.

In this study, we aimed to enhance the wear resistance, thermal conductivity, and anti-/de-icing properties of a gelcoat composite coating by incorporating graphene and boron nitride. The gelcoat graphene coating showed better performance than the gelcoat boron nitride coating and pure gelcoat coating. The improved wear resistance of the gelcoat graphene coating can be attributed to the two-dimensional layer structure of graphene, while the addition of graphene resulted in a threefold increase in the thermal conductivity of the gelcoat composite coating compared to the pure gelcoat coating. The gelcoat composite coatings exhibited a high-water contact angle and low ice adhesive force. It was observed that as the surface roughness increased, the water contact angle also increased. The increase in ice adhesion after abrasion proves that abrasion is always detrimental to de-icing. Despite the extension of icing delay time, the large number of grooves and bumps created by wear results in stronger mechanical interlocking. It is worth mentioning that gelcoat graphene coating still demonstrated lower ice adhesive strength than gelcoat boron nitride coating and pure gelcoat coating. Overall, we successfully developed a gelcoat graphene coating with improved thermal conductivity, wear resistance, and low ice adhesive properties. This novel composite coating has the potential to significantly enhance the efficiency of existing heating technologies for anti-/de-icing applications, thereby reducing energy consumption associated with the turbine blades’ anti-/de-icing system.

In Europe it is approximated that around six million people are active in sport shooting. Currently, most bullets are manufactured out of lead which in many cases is combined with a copper jacket. There are several reasons for using lead; lead is relatively cheap, soft, and has a high density, and is therefore often the ideal material for bullets. Currently, the EU is seeking a restriction or ban on the use of lead in firearm bullets, with the main motivation that hunting with lead bullets might result in lead poisoning of both animals and people. The proposed lead ban will also have a major impact on sport shooting, as it is likely to be subjected to the same regulations. One caliber that has been shown to be very difficult to produce as a lead-free alternative is 22lr (long rifle), which today is the most common caliber for sport shooting in many disciplines. Today, there are not many scientific investigations available which show the performance of lead versus lead-free ammunition in caliber 22lr, even though it would make sense to investigate the possibilities of designing lead-free ammunition before a possible lead ban is further discussed. In this work the performance of two common lead-free 22lr-cartridges is evaluated and shown in comparison with existing lead-based ammunition, with a primary focus on sport shooting. Performance has been evaluated under both summer and winter conditions to cover different sports shooting disciplines. The results of this study clearly show the difficulty of finding lead-free 22lr cartridges with acceptable performance on the market today. In fact, the performance of the tested lead-free ammunition is so poor that the EU’s proposed lead ban could completely ruin shooting as a sport due to a lack of functioning ammunition.

Carbon fiber reinforced polytetrafluoroethylene (CF/PTFE) composites are known for their exceptional tribological performance when sliding against steel or cast iron in inert gas environments. Compared to experiments in humid air, about an order of magnitude lower wear rate and several times lower coefficient of friction have been reported for tests conducted in dry nitrogen and hydrogen. Moreover, trace moisture has been shown to affect the friction and wear significantly of this tribosystem, although a possible effect of oxygen cannot be ruled out due to uncertainties regarding the oxygen concentrations. While several studies have pointed out the environmental sensitivity of CF/PTFE, the understanding of the underlying mechanisms are very limited. The objective of this research is to investigate the individual and combined effect of oxygen and moisture on the tribological behavior of CF/PTFE sliding against steel. Additionally, this study aims to elucidate the underlying mechanisms that govern the environmental sensitivity of the system. Climate-controlled three-pin-on-disc experiments were conducted in nitrogen atmospheres at various concentrations of oxygen and moisture. The tribological results clearly demonstrate that both moisture and oxygen contribute to increased friction and wear. However, the adverse effect was much more pronounced for oxygen than moisture. A qualitative method was developed to estimate the tribofilm coverage on the CF/PTFE surface. Results showed strong correlation between high coverage of strongly adhered tribofilm and low wear rate. Moreover, a loosely adhered tribofilm was observed on top of the CF/PTFE surface in presence of moisture. FTIR analysis indicated that the loosely adhered tribofilm found in the moisture-enriched environment contained a significant amount of adsorbed water, which may explain the lower coefficient of friction in presence of moisture compared to oxygen. The adsorbed water in the loosely adhered tribofilm could be an indication of moisture-driven lubrication by the non-graphitic carbon in the tribofilm.

Triboelectric nanogenerator (TENG) can effectively scavenge ambient mechanical energy with cost, weight, and effectiveness advantages despite critical issues of TENG such as integration to complex components, low current output, and durability. In this work, we designed an adaptive TENG on a mechanical shaft for harvesting rotational energy which can easily assemble and disassemble. The proposed TENG presents an excellent performance for a wide range of rotational speeds (0–2000rpm) and delivers a high power of up to about 80mW (a high short circuit current of 3mA) for a size of 216 cm3, which is high enough for many types of machine condition monitoring purposes. The designed TENG has been evaluated under the noncontact mode of operation within a 0–0.5mm gap between TENG films. The TENG demonstrates excellent electrical stability of 99% without surface wear under noncontact mode within the whole test period for continuous operation of 420,000 cycles. The contact mode with a contact pressure of 0.76 Pa results in 90% electrical stability and apparent surface degradation. Moreover, it is demonstrated that the proposed TENG can power a wireless vibration sensor (60 mW) within 10 minutes energy harvesting for 9 seconds to send data via Bluetooth to a smartphone at up to 30 m, and a wireless temperature sensor (1.2 mW) in real-time for machine condition monitoring.

This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enable investigations of the potential improvements offered by brain-like information processing technologies.

The use of machine learning in condition monitoring became increasingly popular for intelligent fault diagnosis in the last decade, taking advantage of the rapid developments in deep learning.

However, the high computational cost of training and using deep neural networks prevents the use of such solutions for analysing the bulk of data generated by the resource constrained edge devices, i.e., the condition monitoring sensor systems, as only a minor fraction of data can be transmitted to the cloud or edge servers for analysis.

There is an untapped potential to process this data and thereby improve intelligent fault diagnosis models using event-triggered sensing, spiking neural networks, and neuromorphic processors that substantially can improve the energy efficiency and capacity of embedded machine learning condition monitoring solutions.

The proposed co-design model is evaluated on two use-cases involving rolling element bearing failures, one based on a labelled laboratory environment dataset, and one based on a wind turbine drivetrain bearing failure representing a real-world scenario with stochastic changes of machine state and unknown labels of the bearing condition.

By adjusting co-design parameters, the resulting hybrid conventional/neuromorphic model show a comparable accuracy in detection performance for the laboratory dataset compared to the state-of-the-art reported in the literature.

Similarly, for the wind turbine drivetrain dataset a bearing fault detection time comparable to that in previous work is obtained.

This shows the successful implementation of a hybrid conventional/neuromorphic co-design model for condition monitoring applications, offering novel opportunities to investigate performance trade-offs and efficiency improvements enabled by neuromorphic technologies.