Recovery of transverse strain in high–stress tendons following exercise

Background. This study evaluated the time course of recovery of transverse strain in the Achilles and patellar tendons following a bout of resistance exercise. Methods. Seventeen healthy adults underwent sonographic examination of the right patellar (n = 9) or Achilles (n = 8) tendons immediately prior to and following 90 repetitions of weight–bearing exercise. Quadriceps and gastrocnemius exercise were performed against an effective resistance of 175% and 250% body weight, respectively. Sagittal tendon thickness was determined 20 mm from the tendon enthesis and transverse strain was repeatedly monitored over a 24 hour recovery period. Results. Resistance exercise resulted in an immediate decrease in Achilles (t7 = 10.6, P<.01) and patellar (t8 = 8.9, P<.01) tendon thickness, resulting in an average transverse strain of 0.14 ± 0.04 and 0.18 ± 0.05. While the average strain was not significantly different between tendons, older age was associated with a reduced transverse strain response (r=0.63, P<.01). Recovery of transverse strain, in contrast, was prolonged compared with the duration of loading and exponential in nature. The mean primary recovery time was not significantly different between Achilles (6.5 ± 3.2 hours) and patellar (7.1 ± 3.2 hours) tendons and body weight accounted for 62% and 64% of the variation in recovery time, respectively. Discussion. Despite structural and biochemical differences between the Achilles and patellar tendons [1], the mechanisms underlying transverse creep–recovery in vivo appear similar and are highly time dependent. Primary recovery required about 7 hours in healthy tendons, with full recovery requiring up to 24 hours. These in vivo recovery times are similar to those reported for axial creep recovery of the vertebral disc in vitro [2], and may be used clinically to guide physical activity to rest ratios in healthy adults. Optimal ratios for high–stress tendons in clinical populations, however, remain unknown and require further attention in light of the knowledge gained in this study.

Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

Is recovery driven by central or peripheral factors? A role for the brain in recovery following intermittent-sprint exercise

Prolonged intermittent-sprint exercise (i.e., team sports) induce disturbances in skeletal muscle structure and function that are associated with reduced contractile function, a cascade of inflammatory responses, perceptual soreness, and a delayed return to optimal physical performance. In this context, recovery from exercise-induced fatigue is traditionally treated from a peripheral viewpoint, with the regeneration of muscle physiology and other peripheral factors the target of recovery strategies. The direction of this research narrative on post-exercise recovery differs to the increasing emphasis on the complex interaction between both central and peripheral factors regulating exercise intensity during exercise performance. Given the role of the central nervous system (CNS) in motor-unit recruitment during exercise, it too may have an integral role in post-exercise recovery. Indeed, this hypothesis is indirectly supported by an apparent disconnect in time-course changes in physiological and biochemical markers resultant from exercise and the ensuing recovery of exercise performance. Equally, improvements in perceptual recovery, even withstanding the physiological state of recovery, may interact with both feed-forward/feed-back mechanisms to influence subsequent efforts. Considering the research interest afforded to recovery methodologies designed to hasten the return of homeostasis within the muscle, the limited focus on contributors to post-exercise recovery from CNS origins is somewhat surprising. Based on this context, the current review aims to outline the potential contributions of the brain to performance recovery after strenuous exercise.

Theory-based self-management programs for promoting recovery in community-dwelling stroke survivors : a systematic review

Background Post-stroke recovery is demanding. Increasing studies have examined the effectiveness of self-management programs for stroke survivors. However no systematic review has been conducted to summarize the effectiveness of theory-based stroke self-management programs. Objectives The aim is to present the best available research evidence about effectiveness of theory-based self-management programs on community-dwelling stroke survivors’ recovery. Inclusion criteria Types of participants All community-residing adults aged 18 years or above, and had a clinical diagnosis of stroke. Types of interventions Studies which examined effectiveness of a self-management program underpinned by a theoretical or conceptual framework for community-dwelling stroke survivors. Types of studies Randomized controlled trials. Types of outcomes Primary outcomes included health-related quality of life and self-management behaviors. Secondary outcomes included physical (activities of daily living), psychological (self-efficacy, depressive symptoms), and social outcomes (community reintegration, perceived social support). Search Strategy A three-step approach was adopted to identify all relevant published and unpublished studies in English or Chinese. Methodological quality The methodological quality of the included studies was assessed using the Joanna Briggs Institute critical appraisal checklist for experimental studies. Data Collection A standardized JBI data extraction form was used. There was no disagreement between the two reviewers on the data extraction results. Data Synthesis There were incomplete details about the number of participants and the results in two studies, which makes it impossible to perform meta-analysis. A narrative summary of the effectiveness of stroke self-management programs is presented. Results Three studies were included. The key issues of concern in methodological quality included insufficient information about random assignment, allocation concealment, reliability and validity of the measuring instruments, absence of intention-to-treat analysis, and small sample sizes. The three programs were designed based on the Stanford Chronic Disease Self-management program and were underpinned by the principles of self-efficacy. One study showed improvement in the intervention group in family and social roles three months after program completion, and work productivity at six months as measured by the Stroke Specific Quality of Life Scale (SSQOL). The intervention group also had an increased mean self-efficacy score in communicating with physicians six months after program completion. The mean changes from baseline in these variables were significantly different from the control group. No significant difference was found in time spent in aerobic exercise between the intervention and control groups at three and six months after program completion. Another study, using SSQOL, showed a significant interaction effect by treatment and time on family roles, fine motor tasks, self-care, and work productivity. However there was no significant interaction by treatment and time on self-efficacy. The third study showed improvement in quality of life, community participation, and depressive symptoms among the participants receiving the stroke self-management program, Stanford Chronic Disease Self-management program, or usual care six months after program completion. However, there was no significant difference between the groups. Conclusions There is inconclusive evidence about the effectiveness of theory-based stroke self-management programs on community-dwelling stroke survivors’ recovery. However the preliminary evidence suggests potential benefits in improving stroke survivors’ quality of life and self-efficacy.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Health and safety for clean-up and recovery workers

This paper will identify and discuss the major occupational health and safety (OHS) hazards and risks for clean-up and recovery workers. The lessons learned from previous disasters including; the Exxon Valdez oil spill, World Trade Centre (WTC) terrorist attack, Hurricane Katrina and the Deepwater Horizon Gulf of Mexico oil spill will be discussed. The case for an increased level of preparation and planning to mitigate the health risks for clean-up and recovery workers will be presented, based on recurring themes identified in the peer reviewed literature. There are a number of important issues pertaining to the occupational health and safety of workers who are engaged in clean-up and recovery operations following natural and technological disasters. These workers are often exposed to a wide range of occupational health and safety hazards, some of which may be unknown at the time. It is well established that clean-up and recovery operations involve risks of physical injury, for example, from manual handling, mechanical equipment, extreme temperatures, slips, trips and falls. In addition to these well established physical injury risks there are now an increasing number of studies which highlight the risks of longer term or chronic health effects arising from clean-up and recovery work. In particular, follow up studies from the Exxon Valdez oil spill, Hurricane Katrina and the World Trade Centre (WTC) terrorism attack have documented the longer term health consequences of these events. These health effects include respiratory symptoms and musculoskeletal disorders, as well as post traumatic stress disorder (PTSD). In large scale operations many of those workers and supervisors involved have not had any specific occupational health and safety (OHS) training and may not have access to the necessary instruction, personal protective equipment or other appropriate equipment, this is especially true when volunteers are used to form part of the clean-up and recovery workforce. In general, first responders are better equipped and trained than clean-up and recovery workers and some of the training approaches used for the traditional first responders would be relevant for clean-up and recovery workers.

Strategies for rapid congestion recovery using ramp metering

This research investigated strategies for motorway congestion management from a different angle: that is, how to quickly recover motorway from congestion at the end of peak hours, given congestion cannot be eliminated due to excessive demand during the long peak hours nowadays. The project developed a zone recovery strategy using ramp metering for rapid congestion recovery, and a serious of traffic simulation investigations were included to evaluate the developed strategy. The results, from both microscopic and macroscopic simulation, demonstrated the effectiveness of the zone recovery strategy.

Acoustic emission condition monitoring : an application for wind turbine fault detection

Low speed rotating machines which are the most critical components in drive train of wind turbines are often menaced by several technical and environmental defects. These factors contribute to mount the economic requirement for Health Monitoring and Condition Monitoring of the systems. When a defect is happened in such system result in reduced energy loss rates from related process and due to it Condition Monitoring techniques that detecting energy loss are very difficult if not possible to use. However, in the case of Acoustic Emission (AE) technique this issue is partly overcome and is well suited for detecting very small energy release rates. Acoustic Emission (AE) as a technique is more than 50 years old and in this new technology the sounds associated with the failure of materials were detected. Acoustic wave is a non-stationary signal which can discover elastic stress waves in a failure component, capable of online monitoring, and is very sensitive to the fault diagnosis. In this paper the history and background of discovering and developing AE is discussed, different ages of developing AE which include Age of Enlightenment (1950-1967), Golden Age of AE (1967-1980), Period of Transition (1980-Present). In the next section the application of AE condition monitoring in machinery process and various systems that applied AE technique in their health monitoring is discussed. In the end an experimental result is proposed by QUT test rig which an outer race bearing fault was simulated to depict the sensitivity of AE for detecting incipient faults in low speed high frequency machine.

Sustainability in post disaster road infrastructure recovery projects and asset management

Civil infrastructure and especially roads are being impacted with increasing frequency by flood, Tsunami, cyclone related natural and manmade disasters in the world. Responding to such events and in preparing for more regular and intense climate-change induced events in future, the road governing agencies are reviewing how postdisaster road infrastructure recovery projects are best planned and delivered. In particular, there is awareness that rebuilding such infrastructure require sustainable asset management strategies across economic, environmental and social dimensions. A comprehensive asset management framework for pre and post disaster situations can minimize negative impacts on our communities, economy and environment. This research paper is focused on post disaster management in road infrastructures and road infrastructure asset management strategies used by road authorities. Analyzing the implications of disruption to transport network and associated services is an important part of preparing local and regional responses to the impacts of disasters. This research paper will contribute to strategic infrastructure asset planning, management leading to safe, efficient and integrated transport system that supports sustainable economic, social and environmental outcomes. This paper also focuses on proper asset management, governance and engineering principles which should be followed and adopted in post disaster recovery projects to maximize sustainability in environmental, social and economic dimensions.

Application of machine learning technique in wind turbine fault diagnosis

In this study, a machine learning technique called anomaly detection is employed for wind turbine bearing fault detection. Basically, the anomaly detection algorithm is used to recognize the presence of unusual and potentially faulty data in a dataset, which contains two phases: a training phase and a testing phase. Two bearing datasets were used to validate the proposed technique, fault-seeded bearing from a test rig located at Case Western Reserve University to validate the accuracy of the anomaly detection method, and a test to failure data of bearings from the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). The latter data set was used to compare anomaly detection with SVM, a previously well-known applied method, in rapidly finding the incipient faults.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.