Methodology Of Experimental Investigations Of Valve Operation

Tightness, as well as the reliability of the valve plate, is a complex property of the effective operation of compressor cylinders of the first stage and, in general, gas-engine reciprocating compressors. The issue of valve plate tightness is a subject of independent study, since technical and economic efficiency depends on their work. In this connection, only some data obtained under operating conditions are presented in this work.As a research result, it is found that, taking into account the identified requirements for the gas lift system, in order to effectively increase the operating hours of valves with increased tightness of the plate, it is necessary to check and purge the valves. Therefore, each valve in the gas lift compressor station, without subjecting them to cleaning, is first recommended to check for leaks. To confirm the feasibility of checking valve tightness, special equipment is offered for each gas-lift compressor station, a purge chamber, on which the tightness of valve plates is checked.The usefulness and importance of the purge chamber is in preparation of the valve at the gas lift compressor station, which contributes to increased efficiency, safe operation, normal tightness and reliability of its operation

new efficiency opportunities arising from intelligent real time control tools applications the case of compressed air systems energy efficiency in production and use

Abstract Most of the production facilities in Europe make use of compressed air to drive equipment for manufacturing and Compressed Air Systems (CAS) account for about 10% of the total electrical energy consumption of European industries. Therefore, reducing CAS energy consumption is a crucial task to meet the European goals of improving energy efficiency and reducing environmental impact of the industrial sector. This work is part of a wider research activity aimed at developing a strategy to optimize the energy use in CAS. In particular, this paper shows the importance of monitoring energy consumption and control energy use in compressed air generation, to enable energy saving practices, enhance the outcomes of energy management projects, and to guide industries in energy management. We propose a novel procedure in which measured data are compared to a baseline obtained through mathematical modelling (i.e. regression functions) to enable faults detection and energy accounting, through the use of control charts (i.e. variations' control and the Cumulative Sums). The effectiveness of the proposed methodology is demonstrated in a case study, namely the compressed air system of a pharmaceutical manufacturing plant

METHODOLOGY OF EXPERIMENTAL INVESTIGATIONS OF VALVE OPERATION

Tightness, as well as the reliability of the valve plate, is a complex property of the effective operation of compressor cylinders of the first stage and, in general, gas-engine reciprocating compressors. The issue of valve plate tightness is a subject of independent study, since technical and economic efficiency depends on their work. In this connection, only some data obtained under operating conditions are presented in this work. As a research result, it is found that, taking into account the identified requirements for the gas lift system, in order to effectively increase the operating hours of valves with increased tightness of the plate, it is necessary to check and purge the valves. Therefore, each valve in the gas lift compressor station, without subjecting them to cleaning, is first recommended to check for leaks. To confirm the feasibility of checking valve tightness, special equipment is offered for each gas-lift compressor station, a purge chamber, on which the tightness of valve plates is checked. The usefulness and importance of the purge chamber is in preparation of the valve at the gas lift compressor station, which contributes to increased efficiency, safe operation, normal tightness and reliability of its operation

Designing The Estimation of The Need for Spare Parts and Inventory Policy on The D32 CP8 Compressor Machine

Based on damage data owned by PT XYZ, the compressor engine that has a history of high damage is the D32 CP8 Compressor Engine. Critical components of the D32 CP 8 Compressor are determined using a risk matrix. The critical components selected from the D32 CP8 Compressor Engine are Screw Motor, Refrigerant Air Cooler, and Cylinder Bearing Oil Cooler. This study uses the Reliability Centered Spares (RCS), Min-Max Stock, and Reorder Point (ROP) methods. The data collection and processing results obtained the need for critical components in the next 1 year based on the MTTF critical component data. These calculations show that the value of the required spare parts for the Motor Screw, Refrigerant Air Conditioner, and Cylinder Bearing Oil Cooler in a year is 8 components. The minimum Stock of the Screw Motor is 3 components, the maximum stock is 8 components, ReOrder Point point is 4 components. The minimum stock of 4 component air coolers, maximum stock of 7 components, ReOrder Point when 3 components. The minimum stock of a Cylindrical Bearing Oil Cooler is 2 components, the maximum stock is 6 components, and the ReOrder Point is when there are 2 components.Berdasarkan data kerusakan yang dimiliki PT XYZ, mesin kompresor yang memiliki riwayat kerusakan tinggi adalah Mesin Kompresor D32 CP8. Komponen kritis dari Kompresor D32 CP 8 ditentukan menggunakan matriks risiko. Komponen kritis yang dipilih dari Mesin Kompresor D32 CP8 adalah Motor Sekrup, Pendingin Udara Refrigeran, dan Pendingin Oli Bantalan Silinder. Penelitian ini menggunakan metode Reliability Centered Spares (RCS), Min-Max Stock, dan Reorder Point (ROP). Hasil pendataan dan pengolahan yang dilakukan didapatkan kebutuhan komponen kritis dalam 1 tahun ke depan berdasarkan data komponen kritis MTTF. Dari perhitungan tersebut diperoleh nilai kebutuhan suku cadang Motor Sekrup, Pendingin Udara Refrigeran, dan Pendingin Oli Bantalan Silinder dalam setahun adalah 8 komponen. Stock minimum Screw Motor adalah 3 komponen, stock maksimum 8 komponen, ReOrder Point point bila 4 komponen. Stok minimum pendingin udara pendingin 4 komponen, stok maksimum 7 komponen, titik ReOrder Point ketika 3 komponen. Stok minimum Cylindrical Bearing Oil Cooler adalah 2 komponen, stok maksimum 6 komponen, dan titik ReOrder Point saat 2 komponen

A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering

abstract: The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods

A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation

In this paper, we introduce an efficient sparse Gaussian process (E-SGP) for the surrogate modelling of fluid mechanics. This novel Bayesian machine learning algorithm allows efficient model training using databases of different structures. It is a further development of the approximated sparse GP algorithm, combining the concept of efficient GP (E-GP) and variational energy free sparse Gaussian process (VEF-SGP). The developed E-SGP approach exploits the arbitrariness of inducing points and the monotonically increasing nature of the objective function with respect to the number of inducing points in VEF-SGP. By specifying the inducing points on the orthogonal grid/input subspace and using the Kronecker product, E-SGP significantly improves computational efficiency without imposing any constraints on the covariance matrix or increasing the number of parameters that need to be optimised during training. The E-SGP algorithm developed in this paper outperforms E-GP not only in scalability but also in model quality in terms of mean standardized logarithmic loss (MSLL). The computational complexity of E-GP suffers from the cubic growth regarding the growing structured training database. However, E-SGP maintains computational efficiency whilst the resolution of the model, (i.e., the number of inducing points) remains fixed. The examples show that E-SGP produces more accurate predictions in comparison with E-GP when the model resolutions are similar in both. E-GP benefits from more training data but comes with higher computational demands, while E-SGP achieves a comparable level of accuracy but is more computationally efficient, making E-SGP a potentially preferable choice for fluid mechanic problems. Furthermore, E-SGP can produce more reasonable estimates of model uncertainty, whilst E-GP is more likely to produce over-confident predictions

Instantaneous failure mode remaining useful life estimation using non-uniformly sampled measurements from a reciprocating compressor valve failure

One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) , which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create !! and ! metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors

Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors

Fault location in a marine low speed two stroke diesel engine using the characteristic curves method

When a malfunction occurs in a marine main engine system, the impact of the anomaly will propagate through the system, affecting the performance of all relevant components in the system. The phenomenon of fault propagation in the system caused by induced factors can interfere with fault localization, making the latter a difficult task to solve. This paper aims at showing how the "characteristic curves method" is able to properly locate malfunctions also when more malfunctions appear simultaneously. To this end, starting from the working principle of each component of a real marine diesel engine system, comprehensive and reasonable thermal performance parameters are chosen to describe their characteristic curves and include them in a one-dimensional thermodynamic model. In particular, the model of a low-speed two stroke MAN 6S50 MC-C8.1 diesel engine is built using the AVL Boost software and obtaining errors lower than 5% between simulated values and test bench data. The behavior of the engine is simulated considering eight multi-fault concomitant phenomena. On this basis, the fault diagnosis method proposed in this paper is verified. The results show that this diagnosis method can effectively isolate the fault propagation phenomenon in the system and quantify the additional irreversibility caused by the Induced factors. The fault diagnosis index proposed in this paper can quickly locate the abnormal components