scholarly journals Research on Electrostatic Monitoring of Tribo-Contacts with Dynamic Adaptive Fusion Method

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Ruochen Liu ◽  
Han Wang ◽  
Jinwu Zhang ◽  
Shuangshuang Gu ◽  
Jianzhong Sun
Keyword(s):  
Health Management ◽  
High Sensitivity ◽  
Operating Conditions ◽  
Rolling Bearings ◽  
Tribological System ◽  
Monitoring Result ◽  
Electrostatic Charging ◽  
Adaptive Fusion ◽  
Monitoring Platform ◽  
Fusion Approach

Electrostatic monitoring is a unique and rapid developing technique applied in the prognostics and health management of the tribological system based on electrostatic charging and sensing phenomenon. It has considerable advantages in condition monitoring of tribo-contacts with high sensitivity and resolution. Unfortunately, the monitoring result can be affected due to the switch of operating conditions that reduces its accuracy. This paper presents a dynamic adaptive fusion approach, moving window local outlier factor based on electrostatic features to overcome the influence. Life cycle experiments of rolling bearings and railcar gearbox were carried out on an electrostatic monitoring platform. The MWLOF method was used to extract and analyze the experimental data, combined with the Pauta criterion to judge wear faults quantitatively, and compare with other feature extraction results. It is verified that the proposed method can overcome the influence of changes in working conditions on the monitoring results, improve the monitoring sensitivity, and provide an accurate reference for friction and wear faults.

Utilizing Tribological System Parameters as a Harbinger of Distress in Dynamically and Aerothermally Coupled Systems

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Sheridon Haye
Keyword(s):  
Health Management ◽  
Real Data ◽  
Coupled System ◽  
Coupled Systems ◽  
Multiple Systems ◽  
Fuzzy Membership Functions ◽  
Tribological System ◽  
Interacting Systems ◽  
Cross System ◽  
Fusion Approach

This paper summarizes a data fusion approach for utilizing conventional lubrication parameters in an unconventional method for identifying deterioration in a thermally coupled system. Complex machines are composed of multiple systems that are intrinsically dependent. Design of these systems requires expertise in distinct disciplines with a determined focus on meeting system-specific requirements. This expertise focused approach promotes a silo mindset to system design, which is then carried through to the design and implementation of the health management system of these machines. These multidisciplinary interacting systems are traditionally monitored as independent entities, with little advantage taken of the direct and cross-coupled effects. For example, parameters required for lubrication health monitoring include, but are not limited to, oil pressure and temperature. These parameters are critical in determining the health of the lubrication system. However, how these parameters change can be an indicative of the health of interacting systems otherwise considered independent and isolated. By exploring the rationale of the cross-system impacts, physical interactions between these systems (albeit empirical knowledge) can be used for cross-system monitoring. A means of achieving this objective is to utilize parameters that are measured in one system to determine the diagnostic state of another coupled system with limited, or no, system observability. A fuzzy logic fusion approach is employed in this task and was designed and implemented for the above-mentioned purpose. The focus of interest was on the lubrication and hot section interactions with parameters obtained from real machines. Fuzzy membership functions and rules were determined and tuned appropriately from real data and applied to nominal and defective machines.

Effective engagement on process safety fundamentals in challenging times

2021 ◽  
Vol 61 (2) ◽  
pp. 540
Author(s):  
Olivia K. Cary ◽  
Nick Netscher
Keyword(s):  
Human Performance ◽  
Safety Management ◽  
Health Management ◽  
Operating Conditions ◽  
Activity Levels ◽  
Process Safety ◽  
Product Price ◽  
Hydrocarbon Production ◽  
Safety Risks ◽  
Operations And Maintenance

Esso Australia Resources Pty Ltd (EAPL) and BHP Billiton Petroleum (Bass Strait) Pty Ltd own a range of offshore and onshore hydrocarbon production facilities, which have been operated by EAPL for over 50 years. Over this time, EAPL has lived a rich history of process safety experiences, and developed a range of processes and systems to manage process safety risks. Despite technical system refinement and advances across industry we continue to experience process safety events, and manage risks with plant both at the start and end of its lifecycle. Many of our major hazards are inherent to our operations, and do not become lower risk with lower product price or field activity levels. It is therefore critical that we maintain a laser focus on managing process safety risks during this time of unprecedented change, and find impactful opportunities to engage with operations, maintenance and technical teams on their role in process safety. To this end, EAPL have commenced a journey of scenario based process safety management and applying it to our most significant risks. The outcome has been a step change in process safety literacy across our business, an increased awareness of safe operating conditions and a workforce engaged in managing safeguard health. This study shares how a scenario based approach can leverage a traditional safety case and safety management system approach and make process safety personal: Simplifying communication of higher risks and the equipment and processes that keep us safe Clarifying safeguard ownership and responsibilities for safeguard health management Embedding safeguard health management in routine operations and maintenance tasks Strengthening critical safeguards which mostly depend on human performance to be effective

Incipient Surge Detection in Large Volume Energy Systems Based On Wigner-Ville Distribution Evaluated On Vibration Signals

2021 ◽  
Author(s):  
Carlo Alberto Niccolini Marmont Du Haut Champ ◽  
Paolo Silvestri ◽  
Mario L. Ferrari ◽  
Aristide Fausto Massardo
Keyword(s):  
Research Field ◽  
Rotating Stall ◽  
Operating Conditions ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Rotating Machine ◽  
Large Size ◽  
Critical Issues ◽  
Volume Energy ◽  
Transient Operations

Abstract Compressor response investigation in nearly unstable operating conditions, like rotating stall and incipient surge, is a challenging topic nowadays in the turbomachinery research field. Indeed, turbines connected with large-size volumes are affected by critical issues related to surge prevention, particularly during transient operations. Advanced signal-processing operations conducted on vibrational responses provide an insight into possible diagnostic and predictive solutions which can be derived from accelerometer measurements. Indeed, vibrational investigation is largely employed in rotating-machine diagnostics together with time-frequency analysis such as smoothed pseudo-Wigner Ville (SPWVD) time-frequency distribution (TFD) considered in this paper. It is characterized by excellent time and frequency resolutions and thus it is effectively employed in numerous applications in the condition monitoring of machinery. The aim and the innovation of this work regards SPWVD utilization to study turbomachinery behavior in detail in order to identify incipient surge conditions in the centrifugal compressor starting from operational vibrational responses measured at significant plant locations. To this aim, an experimental campaign has been conducted on a T100 microturbine connected with different volume sizes to collect significant data to be analyzed. The results show that SPWVD is able to successfully identify system evolution towards an unstable condition, by recognizing different levels and features of the particular kind of instability that is going to take place within the plant. Instability phenomena regarding rolling bearings have also been identified and their interaction with surge onset has been investigated for diagnostic purposes.

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

scholarly journals Design and Evaluation of LYSO/SiPM LIGHTENING PET Detector with DTI Sampling Method

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5820
Author(s):  
Zhenzhou Deng ◽  
Yushan Deng ◽  
Guandong Chen
Keyword(s):  
High Performance ◽  
Sampling Method ◽  
Average Energy ◽  
High Sensitivity ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Time Interval ◽  
Silicon Photomultiplier ◽  
Experimental Conditions ◽  
Wide Range

Positron emission tomography (PET) has a wide range of applications in the treatment and prevention of major diseases owing to its high sensitivity and excellent resolution. However, there is still much room for optimization in the readout circuit and fast pulse sampling to further improve the performance of the PET scanner. In this work, a LIGHTENING® PET detector using a 13 × 13 lutetium-yttrium oxyorthosilicate (LYSO) crystal array read out by a 6 × 6 silicon photomultiplier (SiPM) array was developed. A novel sampling method, referred to as the dual time interval (DTI) method, is therefore proposed to realize digital acquisition of fast scintillation pulse. A semi-cut light guide was designed, which greatly improves the resolution of the edge region of the crystal array. The obtained flood histogram shown that all the 13 × 13 crystal pixels can be clearly discriminated. The optimum operating conditions for the detector were obtained by comparing the flood histogram quality under different experimental conditions. An average energy resolution (FWHM) of 14.3% and coincidence timing resolution (FWHM) of 972 ps were measured. The experimental results demonstrated that the LIGHTENING® PET detector achieves extremely high resolution which is suitable for the development of a high performance time-of-flight PET scanner.

scholarly journals Laser alloying of bearing steel with boron and self-lubricating addition

2016 ◽  
Vol 36 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Mateusz Kotkowiak ◽  
Adam Piasecki ◽  
Michał Kulka
Keyword(s):  
Wear Resistance ◽  
Calcium Fluoride ◽  
High Hardness ◽  
Bearing Steel ◽  
Operating Conditions ◽  
High Wear Resistance ◽  
Laser Alloying ◽  
Amorphous Boron ◽  
Rolling Bearings ◽  
Borided Layer

Abstract 100CrMnSi6-4 bearing steel has been widely used for many applications, e.g. rolling bearings which work in difficult operating conditions. Therefore, this steel has to be characterized by special properties such as high wear resistance and high hardness. In this study laser-boriding was applied to improve these properties. Laser alloying was conducted as the two step process with two different types of alloying material: amorphous boron only and amorphous boron with addition of calcium fluoride CaF2. At first, the surface was coated with paste including alloying material. Second step of the process consisted in laser re-melting. The surface of sample, coated with the paste, was irradiated by the laser beam. In this study, TRUMPF TLF 2600 Turbo CO2 laser was used. The microstructure, microhardness and wear resistance of both laser-borided layer and laser-borided layer with the addition of calcium fluoride were investigated. The layer, alloyed with boron and CaF2, was characterized by higher wear resistance than the layer after laser boriding only.

scholarly journals Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094432
Author(s):  
Xiaowei Xu ◽  
Xue Qiao ◽  
Nan Zhang ◽  
Jingyi Feng ◽  
Xiaoqing Wang
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Development Trend ◽  
Operating Conditions ◽  
Intelligent Fault Diagnosis ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors

Permanent magnet synchronous motors are the main power output components of electric vehicles. Once a failure occurs, it will affect the vehicle’s power, stability, and safety. While as a complex field-circuit coupling system composed of machine-electric-magnetic-thermal, the permanent magnet synchronous motor of electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency, and communication characteristics make it difficult to diagnose faults. Based on the research of a list of related references, this article reviews the methods of intelligent fault diagnosis for electric vehicle permanent magnet synchronous motors. The research status and development trend of fault diagnosis are analyzed. It provides theoretical basis for motor fault diagnosis and health management in multi-variable working conditions and multi-physics environment.

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