Sensors For Condition Monitoring Research Articles

Antagonistic strategy for enhanced performance polyimide-based triboelectric nanogenerators and wireless self-powered sensors in extremely harsh environments

Triboelectric nanogenerators (TENGs) offer promising green energy solutions for portable microelectronics in the Internet of Everything era. However, their practical applications are limited by low electrical output and stability in harsh environments. This study introduces an innovative antagonistic strategy to enhance TENG performance and stability using thermo-stable polyimide with customizable porous structures (ip-PI). By employing antagonistic solubility, we create ip-PI using chloroform as a solvent for PI and acetone as a solvent for the Poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-co-HFP) template, whereas they both act as nonsolvents for their respective counterparts. Additionally, microbeads-patterned chitosan (mb-CS) is rationally designed and fabricated as morphologically and electrically antagonistic counter surfaces for ip-PI. The resulting antagonist-inspired TENG (AI-TENG) constructed from ip-PI and mb-CS achieves a remarkable average power density of 4 W·m−2, presenting an outstanding 200-fold increase in power compared to TENG made from unstructured flat surfaces. Furthermore, the ip-PI demonstrates excellent temperature resistance, enabling the TENG to operate at extremely high temperatures up to 250 °C, with promising durability of over 10,000 contact-separation cycles. Applications include self-powered machinery condition monitoring sensors and a wireless communication fire forest pre-warning system, showcasing significant advancements in flexible, high-performance TENGs suitable for industrial use in extreme environments.

Wearable Sensors for Physiological Condition and Activity Monitoring

Wearable Sensors for Physiological Condition and Activity Monitoring

Mechanical testing of optical fibers in the gigacycle fatigue regime

The active application of composite smart materials, which include, in addition to the main material carrying the strength load, optical fibers with optic sensors for condition monitoring, makes the task of determining the strength of the fiber optic sensors itself urgent. Such a task is especially acute in systems subjected to long-term alternating (fatigue) loading, when irreversible damage begins to accumulate in the material. This paper shows a method for determining the fatigue life of specialized optical fibers with Bragg gratings in the so-called gigacycle fatigue regime, when the material resists cyclic loading for more than 10^9 cycles.

A piezo-electromagnetic hybrid multi-directional vibration energy harvester in freight trains

Rail freight systems are widely acknowledged for their operational efficiency across various nations. However, a prevailing limitation lies in the absence of power supply within standard freight wagons, impeding the installation of essential sensors to monitor train operation. This paper provides an innovative solution to this issue, where a novel device is developed to harvest vibration energy from freight trains to power operational condition monitoring sensors. The proposed device integrates a piezoelectric energy harvester with an electromagnetic counterpart, utilizing a spring-mass structure to capture vibration energy and transfer it to cantilever beams through magnetic coupling. A cantilever beam with variable width and a distinctive “checkmark” shape is employed to ensure even stress distribution to enhance piezoelectric material efficiency. The optimal spatial configuration between magnets and coils, as well as inter-magnet spacing, is determined through finite element analysis and empirical testing. Experimental results under the excitation with a frequency of 9.5 Hz and a vertical amplitude of 2 mm show the hybrid harvester can generate a maximum power of 3.276 mW. This hybrid energy harvesting system has the potential to provide dependable power to electronic components, including temperature sensors and accelerometers, crucial for monitoring rail freight train operations.

SMS based reservoir condition monitoring sensor system for pumped hydro storage in mini-grids

Solar mini-grids are a cost-effective and efficient method of improving energy access in sub-Saharan Africa, particularly in low-income communities. However, conventional battery storage systems have reduced the affordability of solar mini-grid-generated electricity, necessitating the development of alternative energy storage systems such as pumped hydro storage to replace lithium-ion batteries. This paper introduces a model for an SMS-based fluid parameter monitoring system to provide visibility of reservoir conditions during peak and off-peak periods. The model uses hardware such as voltage regulators, sensors, microcontrollers, and C- programming language software. Sensors monitor various parameters in the reservoir and penstock, transmitting signals to a microcontroller for analysis. The results are relayed to a display unit and personnel's mobile phones for alert notifications, ensuring precise and accurate reporting. Simulation results show the reliability of the design to monitor water levels, water temperature, flow rate, and transmission through SMS to mobile devices for timely reporting. The paper recommends the development and commercialization of this model for larger applications, such as monitoring dams for irrigation and power generation.

Wearable Sensors for Physiological Condition and Activity Monitoring

Rapid technological advancements have transformed the healthcare sector from traditional diagnosis and treatment to personalized health management. Biofluids such as teardrops, sweat, interstitial fluids, and exhaled breath condensate offer a rich source of metabolites that can be linked to the physiological status of an individual. More importantly, these biofluids contain biomarkers similar to those in the blood. Therefore, developing sensors for the noninvasive determination of biofluid‐based metabolites can overcome traditionally invasive and laborious blood‐test‐based diagnostics. In this context, wearable devices offer real‐time and continuous physiological conditions and activity monitoring. The first‐generation wearables included wristwatches capable of tracking heart rate variations, breathing rate, body temperature, stress responses, and sleeping patterns. However, wearable sensors that can accurately measure the metabolites are needed to achieve real‐time analysis of biomarkers. In this review, recent progresses in wearable sensors utilized to monitor metabolites in teardrops, breath condensate, sweat, and interstitial fluids are thoroughly analyzed. More importantly, how metabolites can be selectively detected, quantified, and monitored in real‐time is discussed. Furthermore, the review includes a discussion on the utility of, multifunctional sensors that combine metabolite sensing, human activity monitoring, and on‐demand drug delivery system for theranostic applications.

Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring

Manufacturing steam turbine blades with intricate shapes poses a substantial challenge. These blades significantly impact turbine efficiency, reliability, and productivity. Their precise formation through milling processes demands vigilant monitoring, especially concerning the cutting tool’s condition, which is responsible for shaping the blade profiles and ensuring high-quality outcomes. Direct tool monitoring, however, disrupts productivity, prompting the need for an indirect Tool Condition Monitoring (TCM) system. Such a system becomes essential for detecting tool wear and damage early, ensuring dimensional accuracy and surface smoothness. This experiment monitors tool conditions by analyzing vibrations generated by an endmill cutter while machining martensitic stainless steel (MSS) AISI 420. The setup employs a cost-effective MEMS vibration sensor, the ADXL 345, and Raspberry Pi for real-time online TCM functionality as the signal processor. This study delves into the sensor’s ability to capture vibrations representing actual tool conditions, focusing on the affordability and effectiveness of MEMS-based monitoring. The successful real-time implementation of MEMS-based monitoring could be an easily accessible gateway for manufacturing industries to embrace cloud-based monitoring systems, aligning with current technological trends. This research aims to underscore the viability and potential adoption of affordable MEMS sensors for comprehensive tool condition monitoring, offering a seamless transition toward cloud-integrated monitoring systems for manufacturing industries.

An Online Digital Imaging Excitation Sensor for Wind Turbine Gearbox Wear Condition Monitoring Based on Adaptive Deep Learning Method.

This paper designed and developed an online digital imaging excitation sensor for wind power gearbox wear condition monitoring based on an adaptive deep learning method. A digital imaging excitation sensing image information collection architecture for magnetic particles in lubricating oil was established to characterize the wear condition of mechanical equipment, achieving the real-time online collection of wear particles in lubricating oil. On this basis, a mechanical equipment wear condition diagnosis method based on online wear particle images is proposed, obtaining data from an engineering test platform based on a wind power gearbox. Firstly, a foreground segmentation preprocessing method based on the U-Net network can effectively eliminate the interference of bubbles and dark fields in online wear particle images, providing high-quality segmentation results for subsequent image processing, A total of 1960 wear particle images were collected in the experiment, the average intersection union ratio of the validation set is 0.9299, and the accuracy of the validation set is 0.9799. Secondly, based on the foreground segmentation preprocessing of wear particle images, by using the watered algorithm to obtain the number of particles in each size segment, we obtained the number of magnetic particle grades in three different ranges: 4-38 µm, 39-70 µm, and >70 µm. Thirdly, we proposed a method named multidimensional transformer (MTF) network. Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to obtain the error, and the maintenance strategy is formulated according to the predicted trend. The experimental results show that the predictive performance of our proposed model is better than that of LSTM and TCN. Finally, the online real-time monitoring system triggered three alarms, and at the same time, our offline sampling data analysis was conducted, the accuracy of online real-time monitoring alarms was verified, and the gearbox of the wind turbine was shut down for maintenance and repair.

Low cost MEMS accelerometer and microphone based condition monitoring sensor, with LoRa and Bluetooth Low Energy radio

Vibration-based Condition Monitoring (CM) is an essential tool for identifying potential defects in industrial machinery. However, the implementation of an efficient CM system often necessitates the use of high-cost accelerometers with a large bandwidth. To address this challenge, this study introduces a low-cost CM sensor composed of an ultrasonic MEMS microphone - SPH0641LU and an ADXL1002 MEMS accelerometer. The combination of these two sensor types allows for comparative analysis of the captured data. The SPH641LU microphone is capable of detecting audible vibration signals with a frequency range up to 80 kHz, while the ADXL1002 accelerometer can measure vibrations up to 21 kHz. In addition a three axis ultra low power accelerometer is included, allowing measurement of unbalance or rotating speed, below 200Hz. Moreover, the sensor is designed to operate on battery power and provides the capability for raw data transmission via Bluetooth Low Energy (BLE) or the transmission of pre-processed features from the raw data using LoRaWAN.

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 100Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168000 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.

Classification of the machine state in turning processes by using the acoustic emission

Processing digital information stands as a crucial foundation of Industry 4.0, facilitating a spectrum of activities from monitoring processes to their understanding and optimization. The application of data processing techniques, including feature extraction and classification, coupled with the identification of the most suitable features for specific purposes, continues to pose a significant challenge in the manufacturing sector. This research investigates the suitability of classification methods for machine and tool state classification by employing acoustic emission (AE) sensors during the dry turning of Ti6Al4V. Features such as quantiles, Fourier coefficients, and mel-frequency cepstral coefficients are extracted from the AE signals to facilitate classification. From this features the 20 best are selected for the classification to reduce the dimension of the feature space and redundancy. Algorithms including decision tree, k-nearest-neighbors (KNN), and quadratic discriminant analysis (QDA) are tested for the classification of machine states. Of these, QDA exhibits the highest accuracy at 98.6 %. Nonetheless, an examination of the confusion matrix reveals that certain classes, influenced by imbalanced training data, exhibit a lower prediction accuracy. In summary, the study affirms the potential of AE sensors for machine state recognition and tool condition monitoring. Although QDA emerges as the most acurate classifier, there remains an avenue for refinement, particularly in training data optimization and decision-making processes, to augment accuracy.

PZT-based flexible piezoelectric sensors for real-time condition monitoring

Endowed with high sensitivity, fast response, and excellent flexibility, flexible piezoelectric sensors proffer substantial advantages for establishing efficacious contact with coarse object surfaces. They exhibit remarkable potential for application in wearable devices, motion state recognition, and device vibration monitoring. This paper introduces the development of a novel pliable piezoelectric nanocomposite harnessing modified lead zirconate titanate (PZT) and a flexible photosensitive resin. The PZT-based flexible piezoelectric sensors were fabricated using the light-curing molding technique. By establishing a sensor test platform, the performance parameters of the PZT-based sensors were obtained. Compared to conventional flexible piezoelectric sensors, the output voltage of the PZT-based sensors increased by 1.67 times under the same excitation, resulting in an improved response time and sensitivity. Moreover, the sensors demonstrated an excellent voltage output performance for recognizing human motion states and monitoring electrical equipment vibrations, both in real-time motion state changes and in core vibration under high-frequency operating conditions. These 3D-printed flexible piezoelectric sensors assume paramount significance across diverse domains, including motion science and real-time equipment condition monitoring.

Application of advanced signal processing techniques in vibration analysis and fault diagnosis of multi-dimensional anthropomorphic wind turbines

Abstract Wind turbine operating conditions are complex. To ensure the turbine’s safe operation, it is essential to carry out condition monitoring and fault diagnosis of its vibration. In this paper, from the structure of wind turbines, fault types, and fault formation mechanisms, a wind turbine vibration condition monitoring system is established by designing different vibration condition monitoring sensors and combining them with the Internet of Things technology. The discrete Fourier transform is employed to preprocess the time-frequency data before extracting the specific features of the vibration signal by combining the Hilbert-Huang transform after obtaining the wind turbine vibration signal. The SC-TSFN model with spatio-temporal deep fusion is established to realize the fault diagnosis of wind turbines by combining the replaceable null convolution module, BiLSTM module and the self-attention mechanism. It has been found that when the tertiary meshing frequency fluctuates around 506.98 Hz at a fault characteristic frequency of 16.14 Hz, it indicates a fault in the tertiary high-speed shaft gear. The SC-TSFN model has a fault identification time of approximately 52 days before the actual fault downtime, and the model has a 92.05% accuracy rate for wind turbine fault identification. Relying on the signal processing technology to carry out the wind turbine vibration signal analysis and then input it into the fault identification model can realize the accurate identification of the fault state of the unit and provide technical support for the stable operation of wind turbines.

Energy Management and Networking for Sensor-based Transmission Line State Monitoring

The transmission line condition monitoring sensor network is large in scale and difficult to maintain. Its long-chain network topology differs from general wireless sensor networks, so it is necessary to develop a self-organizing sensor network with self-energy harvesting. In this regard, a three-layer network model is designed. Three types of node models are constructed. The sensor nodes implement round-robin sleep/wake and energy management mechanisms, powered by self-harvested energy. Additionally, reliable routing between nodes is established. Simulation results show that the developed simulation model is suitable for the application of transmission line condition monitoring. The expected functions of each module have been realized, and the stable and reliable transmission of perceived data of key components of transmission lines has been achieved, thereby providing technical and data basis for the deployment of practical networks.

Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing 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.

Identification and evaluation of the characteristics of a selected commercial mems based vibration sensor for the machine condition monitoring

Identification and evaluation of the characteristics of a selected commercial mems based vibration sensor for the machine condition monitoring

All elastomeric pillars-based triboelectric vibration sensor for self-powered broad range machinery condition monitoring

Industrial vibration monitoring is crucial for anticipating equipment failures and ensuring smooth machinery operations. To address this need, we propose a Siloxene-Ecoflex elastomeric macro-pillared self-powered triboelectric vibration sensor (SP-TVS) that offers exceptional detection capabilities, with an ultra-wide frequency range (0.01–15 kHz) and acceleration range (0.1–7.0 g). The SP-TVS combines a facile fabricated elastomeric macro-pillar of nanocomposite paired with a flexible thin film of styrene-ethylene-butylene-styrene (SEBS) to realize the TVS. The SP-TVS exhibits high-frequency response (up to 15 kHz) due to the random high elastic vibration behavior of the macro-pillars and demonstrates excellent acceleration sensitivity of 2.033 V/g (0.1–0.5 g), 1.455 V/g (0.5–2.0 g), and 1.116 V/g (2.0–7.0 g) at r2 = 0.99 in low, medium, and high acceleration ranges and enables subtle vibration amplitude detections. Finally, the SP-TVS has been successfully deployed on various machineries, employing a sophisticated Artificial Intelligence algorithm to process sensor data, performs self-powered machinery identification, vibration inspection, and fault monitoring, showcasing its significant potential for a wide range of industrial monitoring applications.

Fibre Bragg Grating Sensors for Condition Monitoring of High-Voltage Assets: A Review

The high-voltage (HV) assets in the existing power transmission network will experience increased electrical, thermal, environmental and mechanical stresses and, therefore, robust condition monitoring is critical for power system reliability planning. Fibre Bragg grating (FBG) sensors offer a promising technology in HV applications due to their immunity to electromagnetic interference and multiplexing capability. This paper reviews the current technology readiness levels of FBG sensors for condition monitoring of transformers, transmission lines, towers, overhead insulators and power cables, with the aim of stimulating further development and deployment of fibre-based HV asset management systems. Currently, there are several reported cases of FBG sensors used for condition monitoring of HV assets in the field, proving their feasibility for long-term use in the power grid. The review shows that FBG technology is versatile and can facilitate multi-parameter measurements, which will standardise the demodulation equipment and reduce challenges with integrating different sensing technologies.

Multitasking FBG sensors for condition monitoring with a wideband (DC-MHz) interrogation system.

We demonstrate diagnosis of several machine-condition failures using wide-frequency-band interrogation of fiber Bragg grating (FBG) sensors. In collaboration with Israel's national water company Mekorot Ltd., a scaled-down version of a semi-submerged pumping system was constructed. By monitoring broadband signals from DC to ultrasound (>M H z), at different points of the engine and the submersed pump, the system was able to diagnose incipient cavitation, faulty bearings, and submerged dynamic water-level measurements. In addition, a metal embedded FBG sensor was investigated, revealing the potential of using FBGs in applications where bonding is problematic such as bearing housing. These results prove that wideband data acquisition, together with advanced analytics, could open a variety of new applications in the fields of structural health and machine-condition monitoring.

AI Based Real-Time Weather Condition Prediction with Optimized Agricultural Resources

Purpose: Unpredictable and rapid change in weather patterns has great impact on agricultural activities, especially for precision agriculture that results in worsened water resources availability, decreased soil fertility, use of pesticide as well as decreased yield productivity. In attempt to alleviate these challenges, this study aims at developing a real-time weather and farm field data driven Artificial Intelligence (AI) and Internet of Things (IoT) system that analyze, manage and schedule irrigation and fertigation as well as enabling farmers to interact with their farms via Smart phone or PCs to optimize energy and water resources.
 Methodology: The system employs weather condition monitoring sensors such as atmospheric pressure, air temperature, air humidity and wind speed for collecting real-time farm field data and uses Fuzzy Inference System (FIS) to predict rainfall rate at farm area for 24 hours period. The system also gathers field data such as soil moisture content and soil nutrient content and uses the Machine Learning (ML) algorithms to predict the time for irrigation and fertigation. By combining weather and farm field data, the system schedules the irrigation and fertigation activity. In addition, the mobile application is developed for the farmers to interact, control and monitor the farming activities and the data is presented to the farmers in both graphical and numerical formats.
 Findings: The system prototype deployed and tested in the two hectors Maize farm proved that 55% of water, 51% of energy and 20% of fertilizer were saved as well as increases in 20% of Maize yield production compared to previous season.
 Recommendations: Since the current irrigation and fertigation practices are based on predetermined time of the day and threshold values for automatic irrigation, this solution introduced the new concept of real-time and short-term weather forecasting that enables farmers to balance the irrigation period and weather pattern for water and fertilizer resources optimization.