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The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

The purpose of this study is to investigate the transfer of learning among older adults and the importance of physical activity (PA) related to aging in place.

A mixed-methods approach examined 10 older adults aged 65–88, who were receiving occupational therapy and contemplating aging in place. Semistructured interviews determined participants' perceptions of aging in place and PA. Accelerometers assessed levels of PA over 14 days.

Interviews revealed that most participants were aware of the importance of PA but did not specify PA as being a primary contributor to continued independence with aging. Accelerometer data revealed that, on average, 96.7% of the day is spent in sedentary behavior.

Health-care professionals may ask the question, “What will my patient do with the information he or she has learned?” This study was useful to increase understanding of older adults’ learning, lifestyles and effects on aging independently.

As older adults have true expectations of requirements for successful aging in place, realistic levels of PA and transfer of learning could improve the intended outcome of aging independently.

PA is often an overlooked factor for occupational engagement and aging in place and is novel to investigate in combination with interviews.

Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

The purpose of this study is to identify the possible relation between the vibration and the stall by using the vibration response of the airfoil. For this purpose, the root mean square values of the acceleration signals are evaluated to demonstrate the compatibility between the stall angles and the vibration levels.

An experimental study is conducted on NACA 4415 airfoil at Reynolds numbers 69e3, 77e3 and 85e3. Experiments are performed from 0° to 25° of the angles of attack (AoA) for each Reynolds number condition. To observe the change of the vibration values at the stall region clearly, experiments are performed with the AoA ranging from 10° to 25° in 1° increments. Three acceleration sensors are used to obtain the vibration data.

The results show that the increase in the amplitude of the vibration is directly related to the decrease in lift. These findings indicate that this approach could be beneficial in detecting stall on airfoil-type structures.

This study proposes a new approach for detecting stall over the airfoil using the vibration data.

The purpose of this paper is to present investigations on the significant influence of the tube material and fin density on fluid elastic instability and vortex shedding in a parallel triangular finned tube array subjected to water cross flow.

The experiment was conducted on finned tube arrays with a fin height of 6 mm and fin density of 3 fins per inch (fpi) and 9 fpi. A dedicated setup has been developed to examine fluid elastic instability and vortex shedding. Nine parallel triangular tube arrays with a pitch to tube diameter ratio of 1.78 were considered. The plain tube arrays, coarse finned tube arrays and fine finned tube arrays each of steel, copper and aluminium materials were tested. Plain tube arrays were tested to compare the results of the finned tube arrays having an effective tube diameter same as that of the plain tube.

A significant effect of fin density and tube material with a variable mass damping parameter was observed on the instability threshold. In the parallel triangular finned tube array subjected to water cross flow, a delay in the instability threshold was observed with an increase in fin density. For steel and aluminium tube arrays, the natural frequency is 9.77 Hz and 10.38 Hz, which is close to each other, whereas natural frequency of the copper tubes is 7.40 Hz. The Connors’ stability constant K for steel and aluminium tube arrays is 4.78 and 4.87, respectively, whereas it is 5.76 for copper tube arrays, which increases considerably compared to aluminum and steel tube arrays. The existence of vortex shedding is confirmed by comparing experimental results with Owen’s hypothesis and the Strouhal number and Reynolds number relationship.

This paper’s results contribute to understand the effect of tube materials and fin density on fluid elastic instability threshold of finned tube arrays subjected to water cross flow.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

This article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.

Firstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.

By comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.

This article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

The main purpose of this study is to examine the damage behavior of flexural members under different loading conditions. The finite element model is proposed for the prediction of modal parameters, damage assessment and damage detection of flexural members. Moreover, the analysis of flexural members has been done for the sensor arrangement to accurately predict the damage parameters without the laborious work of experimentation in the laboratory.

Beam-like structures are structures that are subjected to flexural loadings that are involved in almost every type of civil engineering construction like buildings, bridges, etc. Experimental Modal Analysis (EMA) is a popular technique to detect damages in structures without requiring tough and complex methods. Experimental work conducted in this study concludes that a structure experiences high changes in modal properties once when cracking occurs and then at the stage where cracks start at the critical neutral axis. Moreover, among the various modal parameters of the flexural members, natural frequency and mode shapes are the viable parameters for the damage detection.

For torsional mode, drop in natural frequency is high for higher damages as compared to low levels. This is because of the opening and closing of cracks in modal testing. When damage occurs in the structure, there is a reduction in the magnitude of the FRF plot. The measure of this drop can also lead to damage assessment in addition to damage detection. The natural frequency of the system is the most reliable modal parameter in detecting damages. However, for damage localization, the next step after damage assessment, mode shapes can be more helpful as compared to all other parameters.

Effect on Dynamic Properties of Flexural Members during the Progressive Deterioration of Reinforced Concrete Structures is studied.