Detection Of Misalignment Research Articles

Acceleration signal with DTCWPT and novel optimize SNR index for diagnosis of misaligned cardan shaft in high-speed train

The cardan shaft transmits rotating motion from motor to gearbox where the axes of rotation of motor and gearbox are not in the same plane. The cardan shaft can be misaligned due to offset at the cross-shaft. The offset causes severe unbalance force on the shaft which eventually leads to catastrophic failure of the whole train. Therefore, it is essential to detect symptom of cardan shaft failure in advance. General diagnostic methods for detecting cardan shaft misalignment are through analysis of vibration signals from sensors located on the rotating system. Unfortunately, the signals are usually corrupted by other mechanical interferences and noise which make vibration analysis unreliable. Hence, it is necessary to eliminate mechanical interferences in the signal and result in improving the detection of shaft misalignment. This study aims to diagnose cardan shaft misalignment by using acceleration signal and dual tree complex wavelet packet transformation (DTCWPT) method to extract the relevant features. The acceleration response of misaligned cardan shaft is analyzed using the multibody dynamics model of Chinese high-speed rail (CRH) built in software. Both DTCWPT and Hilbert transform (HT) methods are applied to decompose the acceleration signal and an envelope of the signal is created. A novel index system is proposed to optimize the signal-to-noise ratio (SNR) index to improve the detection of shaft misalignment. The amplitude of Fourier transform (AFT) coefficient of acceleration signal is then used to assess the degree of shaft misalignment. The results from simulation study and experimental tasks confirmed the capability of the proposed technique in detecting cardan shaft misalignment. This paper presents a new approach to accurately and quantitatively diagnose cardan shaft misalignment.

Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection.

Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. Supplementary data are available at Bioinformatics online.

Detection of Misalignment Using Markov Random Field with Fully Connected Latent Variables in LiDAR-Based Localization

Detection of Misalignment Using Markov Random Field with Fully Connected Latent Variables in LiDAR-Based Localization

ЦИФРОВІ КОМПАРАТОРИ ЧАСТОТИ ТА ФАЗИ В СКЛАДІ СИСТЕМИ СТАБІЛІЗАЦІЇ ПОЛОЖЕННЯ РУХОМОЇ ПЛАТФОРМИ

Urgency of the research. A dual-range, four-quadrant position stabilization system (PSS) of a brushless direct current motor (BLDC) as a part of movable platform (MP), in which the power stage is based on a quasi-resonant pulse converter (QRPC), has the better speed and accuracy characteristics in comparison with hard-switching PWM-based converters. Target setting. The BLDC PSS can be built on the basis of the classic multi-loop control system (MLCS), or on the pulse-frequency (PF-) domain. In PF-domain the task of speed control is transformed into the frequency comparing and matching task, and the position control is transformed into the phasecomparing and matching between the reference signal and the feedback signal. The development of specialized digital blocks of a frequency comparator (FK) and a phase comparator (PC) to perform the above tasks is a non-trivial task. Actual scientific researches and issues analysis. The earliest solutions of the FC, which should be correctly called the ‘frequency detector’ (FD), were implemented mixed-signal (contained both analog and digital nodes) due to their usage in a high-frequency phase-locked loop (PLL) system. A full-featured FC consists of two counters, comparator, a FD block, control finite-state machine (FSM), and a specialized combinational circuit. Uninvestigated parts of general matters defining. Existing FC blocks do not return a quantitative relation between measured frequencies, which can be used to predict dynamics, and are insensitive to the detection of small misalignments between frequencies, that, in turn, creates instability of determining the frequencies equality state. Existing PC blocks are unstable when the frequency of one of the measured signals reaches the system clock frequency, and also do not take into account the edges incoming order of the measured signals. The research objective. The article is devoted to the study and development of the structure of pure-digital FC and PC blocks, which will eliminate the disadvantages of existing solutions and are oriented for integration into PSS with QRPC in the power stage and MLCS operates in a PF-domain. The statement of basic materials. A novel technical solutions is proposed, developed and tested for pure-digital blocks of the FC and PC built on the basis of the field-programmable gate array (FPGA) by means of the hardware description language (VHDL). They allow not only to measure the sign of the inequality of frequency and phase between two periodic signals, but also to obtain the difference numerical values between them. Conclusions. The installation of the FC and PC blocks into the BLDC PSS leads to a significant reducing of FPGA hardware resources utilization and to the high reliability and noise immunity of the MLCS through the unruptured (continiously) nature of the signals. Both the proposed blocks are novel and have eliminated the inherent disadvantages of the existing blocks of the FC and PC due to the installation of additional digital nodes − synchronizers (pulse shorters and edge detectors), and also a hysteresis node that leads to increase the stability and solve the problem of detection near-to-equal frequencies and phases.

Vision-Based Rapid Power Control for a Dynamic Wireless Power Transfer System of Electric Vehicles

Dynamic wireless power transfer brings a refreshing way for charging electric vehicles (EVs) in motion, which can help reduce the amount and bulk of onboard batteries and prolong the vehicle mileage. However, the misalignment between the transmitter coil and receiver coil varies randomly as the vehicle moves, reducing the stability of the output power and the system efficiency. Therefore, a guiding approach is imperative to maintain the coil alignment within an acceptable range. In this paper, a vision-based misalignment detection method is proposed to improve the stability of the system output power and the efficiency. First, the relationships among the system efficiency, output power and mutual inductance were derived in theory. Second, the relationship between the mutual inductance and the misalignment was modeled and simulated using ANSYS Maxwell software. Third, image detection of the ground guideline was used to locate the transmitter coil, and thus obtain the accurate misalignments online. Based on the acquired misalignments, the output power can be rapidly and precisely regulated to the desired value. Finally, the experimental results show that the proposed method can accurately acquire the coil misalignments and then rapidly regulate the system output power in comparison with the conventional discrete proportional-integral control algorithm.

Automated Diagnosis and Measurement of Strabismus in Children

Manual measurements of strabismus are subjective, time consuming, difficult to perform in babies, toddlers, and young children, and rely on the examiner's skill and experience. An automated system, based on eye tracking and dedicated full occlusion glasses, was developed to provide a fast, objective, and easy-to-use alternative to the manual measurements of strabismus. This study tested the efficacy of the system in determining the presence of strabismus in children, as well as its type and the amount of deviation, in addition to differentiating between phorias and tropias. A prospective, masked, inter-rater reliability study. A prospective, masked, cross-sectional study included 69 children, 3-15 years of age. A cover-uncover test and a prism alternating cover test (PACT) for the primary gaze, at a distance of 50cm, were performed by 2 independent, masked examiners and by the automated system. A high correlation was found between the automated and the manual test results (R= 0.9 and P< 0.001 for the horizontal deviation, and R= 0.91 and P < 0.001 for the vertical deviations, with 100% correct identification of the type of deviation). The average automated test duration was 46 seconds. The Bland-Altman plot, used to compare the 2 measurement methods, showed a mean value of-2.9 prism diopters (PD) and a half-width of the 95% limit of agreement of ±11.4 PD. The automated system provides precise detection and measurements of ocular misalignment and differentiated between phorias and tropias. It can be used in participants from the ages of 3 years old and up.

DIAGNOSIS KETIDAKLURUSAN (MISALIGNMENT) POROS MENGGUNAKAN METODE MULTICLASS SUPPORT VECTOR MACHINE (SVM)

&lt;pre&gt;&lt;span lang="EN-US"&gt;Misalignment is a condition where the centerlines of two coupled shafts do not coincide. Misalignment is the commonly fault in rotating machinery. Detection and diagnosis of shaft misalignment is crucial to achieve its optimal performance. The purpose of research is to diagnose shaft misalignment using multiclass support vector machine (SVM). &lt;/span&gt;&lt;span lang="EN-US"&gt;The time-domain vibration signals of a shaft alignment rig with normal, parallel misalignment and angular misalignment of shaft conditions were obtained from vibration measurement signals. The accelerometer was used to measure vibration with &lt;/span&gt;&lt;span lang="EN-US"&gt;a sampling frequency of 20 kHz at the constant speed operation of 1000 rpm&lt;/span&gt;&lt;span lang="EN-US"&gt;. The features of median, RMS, crest factor, variance, kurtosis, shape factor, impulse factor, skewness, range, standard deviation and maximum were extracted from the vibration signal. The &lt;/span&gt;&lt;span lang="EN-US"&gt;Principal Component Analysis (&lt;/span&gt;&lt;span lang="EN-US"&gt;PCA) was applied for reduce the number of variables for data input to principal components with lower dimension. The multiclass SVM with &lt;/span&gt;&lt;span lang="EN-US"&gt;One Against One (OAO) method&lt;/span&gt;&lt;span lang="EN-US"&gt;and linear kernel&lt;/span&gt;&lt;span lang="EN-US"&gt; were used for classification. The results &lt;/span&gt;&lt;span lang="EN-US"&gt;show that SVM &lt;/span&gt;&lt;span lang="EN-US"&gt;for diagnosis of shaft misalignment show a good performance with an accuracy of 100%.&lt;/span&gt;&lt;/pre&gt;

Development of LDA Based Indicator for the Detection of Unbalance and Misalignment at Different Shaft Speeds

In this work, novel Linear Discriminant Analysis (LDA) based indicator has been developed for the detection of unbalance and misalignment at different shaft speeds. The process started with the acquisition of shaft displacement data in fixed time increment. Adaptive resampling of the acquired data was carried to convert time domain data to angular domain. Further, spectrum of angular domain signal was computed. Features were extracted from angular domain vibration signal and its spectrum. Then, LDA of features was done to form linear mapping and 1-D feature. The 1-D feature acts as indicator to discriminate between unbalance and misalignment condition. The experimental result indicate that developed LDA based feature is able to differentiate misalignment and unbalance irrespective of shaft speed by processing data in the angular domain. Using the developed indicator, machine operator will be able to easily determine the presence of unbalance and misalignment in shaft and corrective measures can be taken timely.

Misalignment Detection for Web-Scraped Corpora: A Supervised Regression Approach

To build state-of-the-art Neural Machine Translation (NMT) systems, high-quality parallel sentences are needed. Typically, large amounts of data are scraped from multilingual web sites and aligned into datasets for training. Many tools exist for automatic alignment of such datasets. However, the quality of the resulting aligned corpus can be disappointing. In this paper, we present a tool for automatic misalignment detection (MAD). We treated the task of determining whether a pair of aligned sentences constitutes a genuine translation as a supervised regression problem. We trained our algorithm on a manually labeled dataset in the FR–NL language pair. Our algorithm used shallow features and features obtained after an initial translation step. We showed that both the Levenshtein distance between the target and the translated source, as well as the cosine distance between sentence embeddings of the source and the target were the two most important features for the task of misalignment detection. Using gold standards for alignment, we demonstrated that our model can increase the quality of alignments in a corpus substantially, reaching a precision close to 100%. Finally, we used our tool to investigate the effect of misalignments on NMT performance.

Application of vibration monitoring to the detection of early misalignment and rub failures in a tidal turbine

Research on tidal turbines has been mainly focused on the design for greater energy efficiency for the last ten years. However, achieving a reliable operation is becoming more and more crucial in current studies. This work presents a continuous monitoring of those turbines based on vibrations controls the control, this technique allows implementing predictive maintenance strategies. The vibration response is analysed in a laboratory test rig. That vibration can be produced by a phenomenon as frequent as a misalignment of the shaft as well as their consequences in the early production of partial rub of the rotor/stator. The response obtained shows that one can easily detect a vibration signal with a misalignment in the shaft of 50 μm. Also, the rub of the shaft/seal is detected with the increase of the dynamic stiffness of the system from 12,87 to 14,30 KN/m. Additionally, the resonance frequency also increases its value from 2400 to 2500 rpm. The application of this early detection on the watertight seal of a tidal turbine, would potentially avoid severe hazards in these equipment.

Analysis, Optimization, and Demonstration of a Vehicular Detection System Intended for Dynamic Wireless Charging Applications

To overcome the range limitation associated with electric vehicles (EVs), the emerging technology of dynamic wireless power transfer (DWPT) can be employed. Electrified road infrastructure and wirelessly charged vehicle constitute a complex dynamic system whose successful operation requires coordination between the two subsystems and a certain level of knowledge regarding the EV position and speed on the road. A comprehensive vehicular detection system (DWPT-VDS) operating on magnetic principle and intended for DWPT applications is proposed in this paper. The following functionalities are integrated into the DWPT-VDS: a vehicle detection mechanism, the measurement of the vehicle lateral misalignment, vehicle speed measurement, driver information system (DIS), as well as the wireless communication between a roadside power controller and the DIS. When integrated with a DWPT charging system, the DWPT-VDS allows some critical functions, such as correction of the lateral position of the vehicle by the driver, an extended range of full-power reception for a misaligned vehicle, as well as the smooth transition between adjacent pads. This paper presents a comprehensive study of different coil structures and algorithms for speed and misalignment estimation in a DWPT-VDS, along with their comparison. The objective is to maximize the misalignment detection range for a given size of test coils and provide a robust solution for vehicles with different ground clearances. A three-coil system for vehicle misalignment and speed detection is selected as part of a proof-of-concept design. Part of the system is embedded in the road, and the rest is mounted on a wirelessly charged electric bus. The implemented system has been successfully tested in an outdoor environment. A DIS visualizing speed and misalignment information is also developed and tested to help the driver align the vehicle with the road-embedded primary pads.

Automatic detection and correction of pitch misalignment in wind turbine rotors

Abstract. In this work, a new algorithm is presented to correct for pitch misalignment imbalances of wind turbine rotors. The method uses signals measured in the fixed frame of the machine, typically in the form of accelerations or loads. The amplitude of the one per revolution signal harmonic is used to quantify the imbalance, while its phase is used to locate the unbalanced blade(s). The near linearity of the unknown relationship between harmonic amplitude and pitch misalignment is used to derive a simple algorithm that iteratively rebalances the rotor. This operation is conducted while the machine is in operation, without the need for shutting it down. The method is not only applicable to the case of a single misaligned blade, but also to the generic case of multiple concurrent imbalances. Apart from the availability of acceleration or load sensors, the method requires the ability of the rotor blades to be commanded independently from one another, which is typically possible on many modern machines. The new method is demonstrated in a realistic simulation environment using an aeroservoelastic wind turbine model in a variety of wind and operating conditions.

A support vector machine approach to detect trans-tibial prosthetic misalignment using 3-Dimensional ground reaction force features: A proof of concept

Prosthetists conventionally evaluate alignment based on visual interpretation of patients' gait, which is convenient, but largely subjective and depends on prosthetists' experience. In this paper, we explore the feasibility of using a support vector machine (SVM) approach to automatically detect misalignment of trans-tibial prostheses through ground reaction force (GRF). Alternate classification algorithms with varying kernels and feature sets were compared to assess the suitability for detection of a representative misalignment (six degrees of ankle plantar flexion) from normal alignment. A classical feature selection algorithm, Fisher Score, was further introduced to identify valuable features and reduce the dimension of feature sets. The SVMs achieved a detection accuracy of 96.67% at best within the same subject and 88.89%, respectively, for inter-subject. Combined horizontal and vertical components of GRF features provided the maximum detection accuracies. Propulsion peak force was identified as key variable of gait for misalignment prediction. As a proof of concept, the results demonstrate potential in applying this approach to detect prosthetic misalignment based on gait patterns, and is a step towards future developments of tools for early prevention of misalignment in clinical.

Cause–Effect Analysis for Multiple Misalignment Symptoms in Misalignment Correction Stage

The issue of business and IT alignment (BITA) persists a top concern of researchers and practitioners alike. BITA has not been achieved yet due to dynamic changes in environments and organizations. This paper argues that the misalignment analysis of business and IT, which involves continuously misalignment detection, correction, and prevention, is effective to deal with the dynamics in BITA. The extant research largely pertains to misalignment detection stage. We take one step further and mainly discuss misalignment correction in this paper. A misalignment correction framework is proposed. Within this framework, we especially analyze the cause and effect relationships of the multiple misalignment symptoms proposed by Ori. This framework is beneficial for conducting root-cause analysis and introducing effective mechanisms in the process of BITA.

Design of sTetro: A Modular, Reconfigurable, and Autonomous Staircase Cleaning Robot

The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro (stair Tetro), are presented. The developed robotic platform uses a vertical conveyor mechanism to reconfigure itself and is capable of navigating over flat surfaces as well as staircases, thus significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots. The mechanical design and system architecture are introduced first, followed by a detailed description of system modelling and controller design efforts in sTetro. An autonomy algorithm is also proposed for self-reconfiguration, locomotion, and autonomous navigation of sTetro in the controlled environment, for example, in homes/offices with a flat floor and a straight staircase. A staircase recognition algorithm is presented to distinguish between the surrounding environment and the stairs. The misalignment detection technique of the robot with a front staircase riser is also given, and a feedback from the IMU sensor for misalignment corrective measures is provided. The experiments performed with the sTetro robot demonstrated the efficacy and validity of the developed system models, control, and autonomy approaches.

A novel method based on self-sensing motor drive system for misalignment detection

Abstract In the diagnosis of electromechanical system, traditional vibration analysis methods are affected by the positioning of additional sensors. To overcome this problem, we propose a virtual phase torque (VPT) diagram method based on self-sensing motor drive system. Firstly, the q-axis current is obtained from field oriented control (FOC) process of permanent magnet synchronous motor (PMSM) drive system and used for load torque sensing. Then, fault components extracted from q-axis current and the phase angle from motor encoder are combined together to form the VPT diagram to detect the offset angle of misalignment. Therefore, misalignments in different directions can be classified. Simulations and experiments using a ball screw platform are implemented to validate the proposed method.

Quality control methods for linear accelerator radiation and mechanical axes alignment

The delivery accuracy of highly conformal dose distributions generated using intensity modulation and collimator, gantry, and couch degrees of freedom is directly affected by the quality of the alignment between the radiation beam and the mechanical axes of a linear accelerator. For this purpose, quality control (QC) guidelines recommend a tolerance of ±1mm for the coincidence of the radiation and mechanical isocenters. Traditional QC methods for assessment of radiation and mechanical axes alignment (based on pointer alignment) are time consuming and complex tasks that provide limited accuracy. In this work, an automated test suite based on an analytical model of the linear accelerator motions was developed to streamline the QC of radiation and mechanical axes alignment. The proposed method used the automated analysis of megavoltage images of two simple task-specific phantoms acquired at different linear accelerator settings to determine the coincidence of the radiation and mechanical isocenters. The sensitivity and accuracy of the test suite were validated by introducing actual misalignments on a linear accelerator between the radiation axis and the mechanical axes using both beam steering and mechanical adjustments of the gantry and couch. The validation demonstrated that the new QC method can detect sub-millimeter misalignment between the radiation axis and the three mechanical axes of rotation. A displacement of the radiation source of 0.2mm using beam steering parameters was easily detectable with the proposed collimator rotation axis test. Mechanical misalignments of the gantry and couch rotation axes of the same magnitude (0.2mm) were also detectable using the new gantry and couch rotation axis tests. For the couch rotation axis, the phantom and test design allow detection of both translational and tilt misalignments with the radiation beam axis. For the collimator rotation axis, the test can isolate the misalignment between the beam radiation axis and the mechanical collimator rotation axis from the impact of field size asymmetry. The test suite can be performed in a reasonable time (30-35min) due to simple phantom setup, prescription-based beam delivery, and automated image analysis. As well, it provides a clear description of the relationship between axes. After testing the sensitivity of the test suite to beam steering and mechanical errors, the results of the test suite were used to reduce the misalignment errors of the linac to less than 0.7-mm radius for all axes. The proposed test suite offers sub-millimeter assessment of the coincidence of the radiation and mechanical isocenters and the test automation reduces complexity with improved efficiency. The test suite results can be used to optimize the linear accelerator's radiation to mechanical isocenter alignment by beam steering and mechanical adjustment of gantry and couch.

Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems

The Industry 4.0 vision, grounded on the integration of key technologies and Cyber-Physical-Systems (CPSs), is expected to profoundly modify the manufacturing sector. There is large consensus on the fact that work will change and different skills will be needed. However, whether the organization of work will evolve towards higher responsibility and decision-making of the employees or towards higher technological control is still an open question. The challenge is how to govern this evolution and purposely guide the process of integrating people within CPSs in order to move towards the desired scenario. This paper addresses this challenge, by proposing a methodology to support the design and assessment of different work configurations, jointly considering the uniqueness of human labour and the characteristics of cyber-physical production within a comprehensive framework. The method covers ordinary production as well as irregular scenarios, such as failure detection or maintenance intervention, particularly interesting for human work. The applicability of the method is illustrated through two industrial cases, leading to suggestions for training the personnel and for enhancing the whole cyber- physical-social system. Results include human-centric Key Performance indicators (KPIs) and general guidelines for work design. The approach encourages managers and engineers to clarify their strategy for human resources; develops a multi-perspective awareness on the role of workers; fosters an early detection of possible misalignment between the high-level strategies and the technical interventions on the shop floor. The modelling, analysis and assessment technique developed aims at representing a first step towards formal and quantitative methods to support the design of human work integrated within cyber-physical-systems.

AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data

Geospatial co-registration is a mandatory prerequisite when dealing with remote sensing data. Inter- or intra-sensoral misregistration will negatively affect any subsequent image analysis, specifically when processing multi-sensoral or multi-temporal data. In recent decades, many algorithms have been developed to enable manual, semi- or fully automatic displacement correction. Especially in the context of big data processing and the development of automated processing chains that aim to be applicable to different remote sensing systems, there is a strong need for efficient, accurate and generally usable co-registration. Here, we present AROSICS (Automated and Robust Open-Source Image Co-Registration Software), a Python-based open-source software including an easy-to-use user interface for automatic detection and correction of sub-pixel misalignments between various remote sensing datasets. It is independent of spatial or spectral characteristics and robust against high degrees of cloud coverage and spectral and temporal land cover dynamics. The co-registration is based on phase correlation for sub-pixel shift estimation in the frequency domain utilizing the Fourier shift theorem in a moving-window manner. A dense grid of spatial shift vectors can be created and automatically filtered by combining various validation and quality estimation metrics. Additionally, the software supports the masking of, e.g., clouds and cloud shadows to exclude such areas from spatial shift detection. The software has been tested on more than 9000 satellite images acquired by different sensors. The results are evaluated exemplarily for two inter-sensoral and two intra-sensoral use cases and show registration results in the sub-pixel range with root mean square error fits around 0.3 pixels and better.

The detection of wind turbine shaft misalignment using temperature monitoring

Temperature is a parameter increasingly monitored in wind turbine systems. This paper details a potential temperature monitoring technique for use on shaft couplings. Such condition monitoring methods aid fault detection in other areas of wind turbines. However, application to shaft couplings has not previously been widely researched.A novel temperature measurement technique is outlined, using an infra-red thermometer which can be applied to online condition monitoring. The method was validated and investigated through real time shaft alignment tests. Different modes of misalignment including offset and angular were investigated. This allowed analysis of the relationship between a change in shaft alignment and a change in temperature. The rate of change was found to be a factor of condition through exploring changes in ambient and operating temperatures. The test rig allowed quantification on a small scale and potential causes and effects of temperature changes are discussed.