Browsing by Author "Ruiz Carcel, Cristobal"

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    Data set for "Data-based Detection and Diagnosis of Faults in Linear Actuators"
    (Cranfield University, 2018-03-07 08:40) Ruiz Carcel, Cristobal; Starr, Andrew
    This data set presents the raw original data used in "Data-based detection & diagnosis of faults in Linear actuators".The data was acquired from a linear actuator rig operated using different loading conditions and motion profiles. In addition, three different faults (lack of lubrication, spalling and backlash) were gradually seeded to the system in order to study fault detection and diagnosis capabilities of different algorithms. The data set includes actuator position and motor current measurements for the different conditions mentioned. In addition to the data, the file "Data description.pdf" contains all the details about the test rig set up, cases studied and data structure.
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    Doors datasets
    (Cranfield University, 2020-08-18 11:55) Namoano, Bernadin; Starr, Andrew; Emmanouilidis, Christos; Ruiz Carcel, Cristobal
    Data represents urban train condition monitoring raw data. Data contains, current and door motions during opening and closing periods.
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    Engines datasets
    (Cranfield University, 2020-08-18 11:56) Namoano, Bernadin; Starr, Andrew; Emmanouilidis, Christos; Ruiz Carcel, Cristobal
    Data represents urban DMU trains engines condition monitoring data.
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    Estimation of fine and oversize particle ratio in a heterogeneous compound with acoustic emissions
    (MDPI, 2018-03-13) Nsugbe, Ejay; Ruiz Carcel, Cristobal; Starr, Andrew; Jennions, Ian K.
    The final phase of powder production typically involves a mixing process where all of the particles are combined and agglomerated with a binder to form a single compound. The traditional means of inspecting the physical properties of the final product involves an inspection of the particle sizes using an offline sieving and weighing process. The main downside of this technique, in addition to being an offline-only measurement procedure, is its inability to characterise large agglomerates of powders due to sieve blockage. This work assesses the feasibility of a real-time monitoring approach using a benchtop test rig and a prototype acoustic-based measurement approach to provide information that can be correlated to product quality and provide the opportunity for future process optimisation. Acoustic emission (AE) was chosen as the sensing method due to its low cost, simple setup process, and ease of implementation. The performance of the proposed method was assessed in a series of experiments where the offline quality check results were compared to the AE-based real-time estimations using data acquired from a benchtop powder free flow rig. A designed time domain based signal processing method was used to extract particle size information from the acquired AE signal and the results show that this technique is capable of estimating the required ratio in the washing powder compound with an average absolute error of 6%.
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    Implementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer
    (Cranfield University, 2024-06-07) He, Feiyang; Durazo Cardenas, Isidro; Li, Jian; Ruiz Carcel, Cristobal; Ishola, Ademayowa; Starr, Andrew; Anderson, Robert; Price, Richard
    This research successfully demonstrated autonomous rail inspection feasibility up to Technology Readiness Level (TRL) 7. A prototype integrating an autonomous rail vehicle and Sperry's Ultrasound Testing (UT) system was developed at Cranfield University. It was first tested at Cranfield’s Railways Innovation Test Area (RITA) at TRL 5 and tested at heritage operational railway, in Idridgehay, Derbyshire, UK achieving TRL 7. Experimental works included a 15-meter track test at RITA and nine rounds demonstration of a 250-meter track inspection at Idridgehay, showcasing inspection, localization, navigation accuracy, and defect location precision. The prototype successfully detected artificial rail defect during the demonstration and promptly communicated to command centre via email. We characterised the vehicle performance by measuring the positional error and detection rate. The positional accuracy measurements, verified through GPS and odometry, revealed an odometry-based error of 0.27-3.2 metres and an 8-metre GPS-associated error. The absence of differential GPS and a data fusion approach contributing to these errors. In addition, Weak 4G signal coverage in the fields impacted operator-vehicle communication and data uploading. Future iterations should address these limitations, exploring alternatives for enhanced accuracy and advancing defect-sizing technology.
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    Internet of Things - Enabled visual analytics for linked maintenance and product lifecycle management
    (Elsevier, 2018-09-06) Emmanouilidis, Christos; Bertoncelj, L.; Bevilacqua, M.; Tedeschi, Stefano; Ruiz Carcel, Cristobal
    When closed loop product lifecycle management was first introduced, much effort focused on establishing ways to communicate data between different lifecycle phase activities. The concept of a smart product, able to communicate its own identity and status, had a key role to play to this end. Such a concept has further matured, benefiting from internet things-enabled product lifecycle management advancements. Product data exchanges can now be brought closer to the point of end use consumption, enabling users to become more proactive actors within the product lifecycle management process. This paper presents a conceptual approach and a pilot implementation of how this can be achieved by superimposing middle of life relevant product information to beginning of life product views, such as a 3D product CAD model. In this way, linked maintenance data and knowledge become visual features of a product design representation, facilitating a user’s understanding of middle-of life concepts, such as occurrence of failure modes. The proposed approach can be particularly useful when dealing with product data streams as a natural visual analytics add-in to closed loop product lifecycle management.
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    Investigating precision and accuracy of a robotic inspection and repair system
    (SSRN, 2021-10-20) Rahman, Miftahur; Liu, Haochen; Rahimi, Masoumeh; Ruiz Carcel, Cristobal; Kirkwood, Leigh; Durazo-Cardenas, Isidro; Starr, Andrew
    Robot integration in railway maintenance steps a prominent pavement in high-efficient and low-cost job execution for the infrastructure management. To achieve practical and diverse inspection and repair railway job, a robot manipulator on a locomotive platform is one of the best options. A lot of research has been conducted to find the accuracy and precision of industrial robotic manipulator where the manipulator base is fixed. This paper initiates an exploration of the accuracy and precision of a Robotic Inspection and Repair System (RIRS), which is a novel robotic railway maintenance system integrated with an industrial manipulator (UR10e) with 6 degree-of-freedom, mounting on an Unmanned Ground Vehicle (UGV) (Warthog) and specially designed trolley. In this research, a mimic track visual inspection test using QR code detection is adopted and implemented by an arm-mounted monocular camera. Then a sequential pose moves with multiple payload weights on the manipulator end has been performed as a performance measurement of repair jobs using a vision-based position tracking algorithm. The measurement results demonstrate that RIRS can maintain accurate and consistent performance in both defect position inspection and repair moves with diverse payloads. For inspection the positional error was only 0.27% while for repair moves the end-effector can reach the same position within 1mm. This research establishes a foundation for system command & control development and supporting more practical railway jobs deployment towards full autonomy for RIRS in the future.
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    Localisation and navigation framework for autonomous railway robotic inspection and repair system
    (SSRN, 2021-10-20) Rahimi, Masoumeh; Liu, Haochen; Rahman, Miftahur; Ruiz Carcel, Cristobal; Durazo-Cardenas, Isidro; Starr, Andrew; Hall, Amanda; Anderson, Robert
    In the path towards the intelligent industrial 4.0, the railway industry is keen to develop intelligent asset management strategies for digitalization and smart management for rail infrastructure. It aims to both reduce the cost and exposure of human-labor, associated with track maintenance risk, as well as increase the autonomy and accuracy for the railway inspection and repair job. A Robotic Inspection and Repair System (RIRS) is proposed to undertake the automated railway maintenance consisting of the autonomous off-track travel between base workshop and track, road-rail conversion, autonomous on-track inspection, and repair as well as remote communicating to railway signaling system and infrastructure system. This paper presents a localization and navigation framework for this new autonomous system; applied to the mentioned railway maintenance job. This system comprises a commercial Unmanned Ground Vehicle (UGV, named Warthog) with a robotic manipulator (UR10e), and multiple onboard sensors including Lidar, camera, RTK GNSS, IMU, wheel odometry, and multiple types of cameras. An adaptive trolley is also designed for the purpose of road-rail conversion. This research also focuses on how to increase accuracy for the support of track defect detection and localization.
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    Low-cost vibration sensor with low frequency resonance for condition monitoring of low speed bearings: a feasibility study
    (IET - The Institution of Engineering and Technology, 2023-09-27) Ompusunggu, Agusmian Partogi; Ruiz Carcel, Cristobal
    Condition monitoring (CM) of rolling element bearings (REBs) rotating at low speeds poses some challenges in practice because of the low signal-to-noise ratio produced by the faults. Both vibration-based and ultrasound/acoustic emission (AE) based sensing techniques have been proposed in the literature to detect faults in rolling element bearings running at low speeds. The vibration-based technique generally works within the frequency band from 0 to 20 kHz. Meanwhile, the ultrasound/AE-based techniques work in a very high-frequency band, from 20 kHz up to 1 MHz. Consequently, processing ultrasound/AE sensor data requires more computational resources compared to processing vibration data. Moreover, the hardware investment to build an ultrasound/AE-based CM system is more expensive than that of a vibration-based CM system. Since hardware and software cost is one of the main bottlenecks of the adoption of CM systems in the industry, it is, therefore, necessary to develop a cost-effective vibration-based CM system for critical bearings mounted on low-rotational speed machines. The paper presents a feasibility study in evaluating the performance of an off-the-shelf low-cost vibration sensor (10 – 20 times cheaper than high-end vibration sensors) with a low resonance frequency (around 75 Hz) to diagnose faults on REBs operating at a low rotational speed of 30 - 60 rpm. The low-resonance frequency characteristic of the low-cost sensor allows us to acquire the data at a low sampling rate of 400 Hz. A theoretical justification of why a vibration sensor with low-resonance frequency can still be effective for low-speed bearing fault diagnosis is given. This feasibility was experimentally validated on a test rig on which an REB with a seeded fault on the outer race was tested as a case study. A high-end vibration sensor (accelerometer) acquired at a 20 kHz sampling rate was also used as a benchmark. The vibration signals measured by the low-cost and high-end sensors in four different operating conditions were analysed with the well-established envelope analysis. In addition, the high-end sensor signals were also analysed with the state-of-the-art Spectral Correlation (SC) technique to compute the Enhanced Envelope Spectrum (EES) for bearing fault detection and diagnosis. The results confirmed that the bearing fault could be successfully detected and diagnosed in all the test conditions by the low-cost sensor analysed with the envelope analysis technique. On the other hand, the high-end sensor analysed with the SC technique could only diagnose the bearing fault for the least challenging test condition. The outstanding diagnostic capability of the low-cost sensor sampled at a low sampling rate has set a milestone that would enable the future development of a low-cost CM system for low-speed bearing applications.
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    Online particle size distribution estimation of a mixture of similar sized particles with acoustic emissions
    (IOP Publishing, 2017-08-29) Nsugbe, Ejay; Starr, Andrew; Jennions, Ian K.; Ruiz Carcel, Cristobal
    Particle processing plants regard the Particle Size Distribution (PSD) as a key quality factor as it influences the bulk and flow properties of the particles. In this work, Acoustic Emission (AE) is used to estimate the PSD of a mixture that comprise of similar sized particles. The experiments involved the use of regular sized particles (glass beads) and with the aid of a time domain based threshold analysis of the particle impacts the PSD of the mixtures could be estimated.
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    Particle size distribution estimation of a mixture of regular and irregular sized particles using acoustic emissions
    (Elsevier, 2017-09-18) Nsugbe, Ejay; Starr, Andrew; Jennions, Ian K.; Ruiz Carcel, Cristobal
    This works investigates the possibility of using Acoustic Emissions (AE) to estimate the Particle Size Distribution (PSD) of a mixture of particles that comprise of particles of different densities and geometry. The experiments carried out involved the mixture of a set of glass and polyethylene particles that ranged from 150-212 microns and 150-250microns respectively and an experimental rig that allowed the free fall of a continuous stream of particles on a target plate which the AE sensor was placed. By using a time domain based multiple threshold method, it was observed that the PSD of the particles in the mixture could be estimated.
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    Size differentiation of a continuous stream of particles using acoustic emissions
    (Institute of Physics, 2016-12-01) Nsugbe, Ejay; Starr, Andrew; Foote, Peter; Ruiz Carcel, Cristobal; Jennions, Ian K.
    Procter and Gamble (P&G) require an online system that can monitor the particle size distribution of their washing powder mixing process. This would enable the process to take a closed loop form which would enable process optimisation to take place in real time. Acoustic Emission (AE) was selected as the sensing method due to its non-invasive nature and primary sensitivity to frequencies which particle events emanate. This work details the results of the first experiment carried out in this research project. This experiment involved the use of AE to distinguish between the sizes of sieved polyethylene particle (53-250microns) and glass beads (150-600microns) which were dispensed on a target plate using a funnel. By conducting a threshold analysis of the impact peaks in the signal, the sizes of the particles could be distinguished and a signal feature was found which could be directly linked to the sizes of the particles.
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    Size differentiation of a continuous stream of particles using acoustic emissions
    (Acoustical Society of America, 2016-04-30) Nsugbe, Ejay; Starr, Andrew G.; Foote, Peter; Ruiz Carcel, Cristobal; Jennions, Ian K.
    Procter and Gamble (P&G) requires an online system that can monitor the particle size distribution of their washing powder mixing process. This would enable the process to take a closed loop form which would enable process optimization to take place in real time. Acoustic emission (AE) was selected as the sensing method due to its non-invasive nature and primary sensitivity to frequencies which particle events emanate. This work details the results of the first experiment carried out in this research project. The first experiment involved the use of AE to distinguish sieved particle which ranged from 53 to 250 microns and were dispensed on a target plate using a funnel. By conducting a threshold analysis of the peaks in the signal, the sizes of the particles could be distinguished and a signal feature was found which could be directly linked to the sizes of the particles.
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    Statistical process monitoring of a multiphase flow facility
    (Elsevier, 2015-06-16) Ruiz Carcel, Cristobal; Cao, Yi; Harrison, David; Lao, Liyun; Samuel, Raphael
    Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms.