Workers’ fatigue is a significant problem in physically demanding occupations. Physical fatigue is known to result in the inability to maintain proper posture and working technique. Consequently, workers lose their ability to safely and effectively perform their duties. Thus, understanding the physical demands of labor-intensive work is of great importance in protecting workers’ safety, and maintaining productivity. Current fatigue assessments methods, including surveys and questionnaires, are subjective and lack reliability. Objective fatigue assessments based on physiological data are more reliable, however they are cumbersome to implement in real work conditions. There is a need for an objective fatigue assessment method that can monitor physical fatigue with minimal intrusion. The goal of this study was to investigate whether jerk, the time-derivative of acceleration, can be used to objectively detect physical fatigue. A pilot study on masons was conducted to determine if physical fatigue can be detected by changes in jerk values. Ten participants performed a bricklaying task using forty-five concrete masonry units (CMU). Seven body segments, namely the hands, forearms, upper arms, and pelvis, were selected for placement of IMU sensors to measure the segment accelerations. Jerk was calculated from the measured acceleration via numerical differentiation. Characteristic values of the jerk at the beginning and end of the bricklaying task were obtained to represent the rested and fatigued states. They were then compared for significant differences. Jerk values calculated from the IMU sensors located on the upper arms and pelvis showed significant differences between rested and fatigued states. The results of this pilot study indicate that the characteristic jerk can be used to detect physical fatigue, however caution must be taken in selecting sensor locations to reduce the influence of spurious signals.

Recent developments in imaging systems have seen the implementation of a radar matched-filtering approach. The goal of the imaging system is to obtain information about an unknown object embedded in the system, by controlling the parameters of the input and measuring the response to the known input. The main merit of using matched filtering in imaging systems is the improvement of Signal to Noise Ratio (SNR). However, the correlation process used in matched filtering may result in a loss of resolution. One way to compensate for lost resolution is via pulse compression. Linear frequency modulated sinusoidal waveforms (chirps) have the property of pulse compression after correlation. Hence, both SNR and resolution can be enhanced by matched-filtering and pulse compression with a chirp. However, the theory behind the effect of chirp parameters on resolution is still not clear. In this paper, a one-dimensional theory of matched-filter imaging with a pulse compressed linear frequency modulated sinusoidal chirp is developed. The effect of the chirp parameters on the corresponding signal is investigated, and guidelines for choosing the chirp parameters for resolution considerations are given based on the developed theory and simulations. The results showed that by manipulating the center frequency, bandwidth, and duration of the chirp, the resolution can be easily enhanced.

A scanning force microscope combining commercial AFM probes, printed circuit boards, and electrostatic actuation and detection is proposed and demonstrated. The electrostatic actuator is formed by the AFM probe and a fixed trace on a PCB. It is driven by a biased harmonic voltage with a frequency close to the probe’s resonant frequency. The separation distance between the probe tip and the specimen surface is managed to perform tapping mode scanning. A lock-in amplifier measures the second harmonic of the actuator current as the sense signal in an open-loop scheme. Preliminary results produced by the current’s magnitude and phase resemble the specimen’s topography.

Active, transtibial prostheses typically use finite state control algorithms that struggle with cadence and gait variability of the amputee. Recent work in artificial neural networks (ANN) have shown the possibility to predict the users intent based on EMG activity and the current position of the ankle, which can be used as an input signal into an improved controller. This paper examines how to implement an ANN signal into a zero order impedance controller, i.e., a stiffness controller, on a specific active transtibial prosthesis. The prosthesis incorporates a linear spiral spring in parallel with a four-bar mechanism. In order to implement stiffness control, the spring was moved to being in series with the four-bar mechanism to establish a relationship between the torque of the spring and the position of the motor. To ensure stiffness control is feasible, a MATLAB Simulink model of the system was created to test the robustness of the controller and the effect of moving the spring from parallel to series. The robustness of the controller was verified as the ankle position and torque requirements are met in the simulation. The Simulink model accurately models the new system and can be used in the future to optimize the motor or the four-bar mechanism for this new type of control.

Compressive sensing (CS) theory allows measurement of sparse signals with a sampling rate far lower than the Nyquist sampling frequency. This could reduce the burden of local storage and remote transmitting. The periodic impacts generated in rolling element bearing local faults are obviously sparse in the time domain. According to this sparse feature, a rolling element bearing fault feature extraction method based on CS theory is proposed in the paper. Utilizing the shift invariant dictionary learning algorithm and the periodic presentation characteristic of local faults of roller bearings, a shift-invariant dictionary of which each atom contains only one impact pattern is constructed to represent the fault impact as sparsely as possible. The limited degree of sparsity is utilized to reconstruct the feature components based on compressive sampling matching pursuit (CoSaMP) method, realizing the diagnosis of the roller bearing impact fault. A simulation was used to analyze the effects of parameters such as sparsity, SNR and compressive rate on the proposed method and prove the effectiveness of the proposed method.

Much research has been carried out to investigate the dynamical response of a gear system because of its importance on vibration feature analysis. It is well known that the gearbox casing is one of the most important components of the gear system and plays an important role in signal propagation. However, its effects have widely been neglected within the dynamic simulations and few dynamic models have considered the gearbox casing when modeling a gear transmission. This paper proposes a gear transmission dynamical model with the consideration of the effects of gearbox casing. The proposed dynamical model incorporates TVMS, a time-varying load sharing ratio, as well as dynamic tooth contact friction forces, friction moments and dynamic mesh damping coefficients. The proposed gear dynamical model is validated by comparison with responses obtained from experimental test rigs under different speed conditions. Comparisons indicate that the responses of the proposed dynamical model are consistent with experimental results, in both time and frequency domains under different rotation speeds.

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

Due to its special modulation mechanism with multiple units (eg. shafts, gears, etc.) under various conditions, the related fault information of gear fault would distribute in a broad frequency band. In this manner, it is not easy about accurately detecting the early-stage gear fault by detecting the fault frequency in a limited frequency band. In this paper, a new spectral analysis, called multiscale sparse spectrum (MSS), is proposed to achieve fault frequency detection in a sound way. The overall frequency information about the raw signal is firstly sensed by a series of frequency-window function, which can be reached by short-frequency Fourier transform. Then, according to orthogonal matching pursuit, harmonic atoms are further employed to sparsely mine the modulation components from these multiscale pseudo mono-components. Finally, MSS is proposed to synthesize the existing harmonic-related components. Furthermore, a synthesized sparse spectrum (SSS) is obtained by searching the frequency-frequency ridge from MSS. Compared with EMD and fast-kurtogram analysis, the results show the effectiveness of the proposed method of gear fault detection.

Control of flexible systems is effected by design requirements and also manufacturing aspects. The dynamics and control of such systems are challenging, especially in the case of an inverted flexible pendulum system. The experimental study of the dynamical behavior of this kind of system showing jumping phenomenon between three equilibria is not considered in detail in literatures so far. The paper focuses on studying the effects of some parameters to the dynamics of the flexible pendulum. By varying the excitation parameters, control parameters, as well as other distinguished mechanical parameters, different phenomena are observed in experiments discussed in this contribution. In this study, a custom built inverted flexible pendulum on cart system under PID-controlled harmonic excitation is considered. Data are collected from both cart excitation signal and displacement of the pendulum, also to observe their correlation towards jumping behavior. Effects of the variation of the parameters leading to changes in chaotic jumping patterns. Multiple equilibria are observed and analyzed. It can be concluded that depending on the excitation amplitudes, frequencies, and controller parameters, the minimum of two equilibria with an unstable third equilibrium can be detected while jumping phenomena between the equilibria are observed. Questions about the stimulation of the jumping by impulses resulting from imperfect sinusoidal excitation due to control limitations are discussed.

Time-frequency method is a good tool to analysis the vibration signal for condition monitoring of rotational machinery under non-stationary conditions. However, the fault feature of bearing cannot be directly revealed using the time-frequency method, because the fault causes complex modulation of resonance frequency. To resolve this problem, this paper proposes a joint application of Spectral Kurtosis (SK) and Velocity Synchrosqueezing Transform (VST). The vibration is firstly processed by spectral kurtosis method to extract the impulse features of the signal. After this, amplitude demodulation techniques are applied to obtain the impulse envelope. Finally the VST is applied to reveal the time-frequency feature of the envelope signal. The fault, if any, can be diagnosed by identifying the uncovered signal components. The effectiveness of the proposed method is validated using experimental signals collected under non-stationary condition.

The planetary gearbox is one of the key components in the rotating machinery. The planetary gearbox is prone to malfunction, which increases downtime and repair costs. Hence, the fault diagnosis of the planetary gearbox is an important research topic. The acquired signal from the planetary gearbox exhibit strongly time-variant and nonstationary features since the planetary gearbox usually works at time-varying speeds. In this study, a new time-frequency analysis method is proposed. This method takes the spectrum shape into account and partitions the time-frequency into several components. Then the fault feature of the planetary gearbox is detected by analyzing the decomposed components. The simulated signal and the experimental signals under nonstationary conditions are analyzed to verify the effectiveness the proposed method. Results show that the proposed method can efficiently extract the fault feature of the planet gear.

The kurtogram is a spectral analysis tool used to detect non-stationarities in a signal. It can be effectively used to determine the optimal filter for bearing fault feature extraction from a blurred vibration signal, since the transients of the bearing fault-induced signal can be regarded as non-stationary. However, the effectiveness of the kurtogram is diminished when the signal is collected from a bearing operating under time-varying speed conditions. There is a need to improve the performance of the kurtogram under time-varying speed conditions. In this paper, a short-time kurtogram method is proposed for bearing fault feature extraction under time-varying speed conditions. The performance of the short-time kurtogram is examined with experimental data. The results demonstrate that the short-time kurtogram can effectively be used to extract bearing fault features under time-varying speed conditions.

At present, the power wagon is confronted with several problems: small brake torque at low speeds, output instability of random loading and slow response speed. Aiming at the existing problems of current control performance for loading system of power wagon, the paper proposes a back propagation (BP) neural net PID algorithm. The dynamic analysis of power wagon loading system was presented. A mathematical model of the loading system is established by use of the classical control theory. Through simulation and analysis, the dynamic response of the loading system is researched by using step signal, sine signal and field-load spectrum stochastic signal as system input respectively. Meanwhile, through MATLAB simulation experiment, compares and analysis with superiorities and characteristics between BP neural net PID control method and traditional PID control method. On the basis of this study, the power wagon was modified based on YTO-LX1304. The road test of the YTO-MF554 tractor traction property was performed on the concrete by using the power wagon. Experimental results show that the system output traction has a good follow effect in comparison with input load. The BP neural net PID algorithm can improve the system’s dynamical performance and its static accuracy in comparison with the traditional PID control. The simulation results show that system delay time is 0.12s and maximum overshoot is 3.8%. Road test results show that system delay time is 0.22s and maximum overshoot is 4.2%. The BP neural net PID algorithm has better control performance for second-order plant with time-delay time-varying system.

In Russia, 36% [1] of all accidents are a collision with a pedestrian. Vehicle structural elements limit visibility to drivers and do not allow fully assessing the traffic situation, forming blind zones. It especially applies to commercial vehicles with large dimensions. The system allowing to expand visibility of the car due to use of displays in windscreen pillars is presented in this article. It is proposed to use the pedestrian recognition system integrated into the windscreen pillars based on neural networks. The system allows to detect and estimate distance to the objects and to give warning signals about possible collision. It consists of an on-board computer, video cameras and output devices. Interior elements of a commercial vehicle were developed and installed. External side mirrors were replaced with video cameras. The exterior elements were designed and installed. Further there were carried out experimental researches of the system.

Quick locomotion has always been a challenge for humanoid robots. Most of the work has been done to improve the efficiency of the walking gaits. Recently, additional equipment like skates are increasingly being used to speed up location, but they also make the system highly unstable. This paper describes the development of a statically stable skating gait to facilitate movement across plain surfaces, such as roads and hard ice. The new gait utilises the non-holonomic nature of a wheel (or blade of an ice skate). The proposed motion of the skates on the ground plane enables it to propel the robot forward without lifting its leg. Kinematic and dynamic equations of an equivalent model are formulated. Further, the paper discusses the relationship between different input signals and their corresponding output gaits. Multibody dynamics software is then used to simulate and verify the results for various scenarios. The design of an equivalent model with three degrees of freedom is then analysed and discussed for practical testing. Finally, the algorithm was tested on a fabricated robot.

The current paper provides some on-field measurements regarding the quantification of the dissipated power during the damping process of a traditional viscous shock absorber. In this regard, the HAVAL H8 SUV was driven for several trips on the Nanhu campus arena considering a velocity range of 20–50 km/h. Furthermore, two species of campus road sections were selected during the fabricated tests; straight road section with and without a speed bump. The acceleration signals of the rear-right suspension system (body and wheel) were acquired as the average power dissipation trend could be calculated from the relative suspension velocity. The findings of this investigation indicate that the average dissipated power of a traditional shock absorber can be in a range of 10–90 W for a speed range of 20–50 km/h driving on a campus road section free of speed bumps. Whilst, for another road segment with one speed bump, the shock absorber dissipated a kinetic energy between 40–140 W for a velocity range of 20–50 km/h. Suggesting that an average overall dissipated power of 160–560 W is available by means of the traditional shock absorbers. The results are of strategic interest for the researchers and vehicle manufacturers for further considerations in terms of regenerative suspension systems where a part of this energy could be harvested instead of being wholly dissipated.

The obtaining of the overall transfer equation of a linear controlled multibody system is one of the key problems when the transfer matrix method for multibody systems (MSTMM) is applied to study controlled multibody dynamics. This paper applies the theory of matrix signal flow graphs (MSFG) to MSTMM. The transfer matrix signal flow graphs (TMSFG) of a mechanical element and a control subsystem can be drawn according to their transfer equations. By merging the nodes corresponding to the same state vector, the TMSFG of the entire system can be obtained. Eventually, the overall transfer equation of the linear controlled system can be systematically obtained according to the reduction rules of MSFG. An example is taken to sketch the idea and the simulation results are compared with other ordinary dynamics methods to validate the proposed method. This paper lays a foundation for automatic deduction of overall transfer equations of linear controlled multibody systems.

To convert induced surface stress of a bio-functionalized microcantilever into an electrical signal; U shaped piezoresistive detection technique is mostly preferred over other techniques due to its several advantages. But the inherent disadvantage of this technique is thermal stress sensitivity as a source of noise which reduces its signal to noise ratio [SNR]. Polymer microcantilever has larger stress sensitivity due to its low youngs modulus of elasticity. Varying thickness cantilever satisfy this desired criteria as compared to other configurations of cantilever and has high resonance frequency. Taking all these aspects into consideration, the objectives of proposed study is to design and simulate multilayer varying thickness microcantilever. The numerical analysis is performed using CoventorWare a commercial MEMS design and FEM Multiphysics tool. It is observed that the SNR of the varying thickness microcantilevers design is improved by more than 70%, over normal rectangular design.

Relevant research has demonstrated that more potential benefits can be achieved when energy and information are transacted and exchanged locally among different energy consumers. With increasing number of electric vehicles (EVs), various models and solution strategies have been developed for collaboration between building and EV charging station to achieve greater energy efficiency. However, most of the existing research employs centralized decision model which is time consuming for large scale problems and cannot protect private information for each participator. To bridge these research gaps, a guided particle swarm optimizer based distributed decision approach is proposed to study the energy transaction between building and EV charging station. In the proposed decision approach, the marginal price signal of transactive energy is collected to guide iterative direction of particle’s velocity and position which can maximally protect private information of building and EV charging station. A study case based on a commercial building and a nearby charging station in Chicago area is designed for illustration. The experimental results demonstrate that our proposed marginal price guided particle swarm optimizer is more stable and efficient comparing with canonical particle swarm optimizer and two state-of-the-art distributed decision algorithms.

Weak fault detection is crucial to incipient mechanical fault diagnosis. In order to extract weak fault signals, a method named improved Re-scaling Frequency Stochastic Resonance (IRFSR) is proposed in this paper. The method consists of four steps: (i) Frequency Information Exchange (FIE); (ii) Amplitude Coefficient Adjustment; (iii) Re-scaling Frequency Stochastic Resonance (RFSR); and (iv) Frequency Information Recovery. By means of the exchange of frequency information, the high-frequency information of the target signal is accordingly transferred to the low frequency band which can be rescaled to satisfy the small-parameter limits of classical stochastic resonance. Then IRFSR is able to overcome the limitation of RFSR, which is that the sampling frequency of RFSR is at least 50 times greater than the frequency of the target signal. Numerical results are reported to evaluate the effectiveness of IRFSR to detect the target signal with higher frequency at a low sampling frequency as compared with RFSR. And the feasibility of the IRFSR in incipient fault detection is demonstrated using case history data obtained from a sliding bearing test rig.