Comparison of different classification algorithms for fault detection and fault isolation in complex systems

Due to the lack of sufficient results seen in literature, feature extraction and classification methods of hydraulic systems appears to be somewhat challenging. This paper compares the performance of three classifiers (namely linear support vector machine (SVM), distance-weighted k-nearest neighbor (WKNN), and decision tree (DT) using data from optimized and non-optimized sensor set solutions. The algorithms are trained with known data and then tested with unknown data for different scenarios characterizing faults with different degrees of severity. This investigation is based solely on a data-driven approach and relies on data sets that are taken from experiments on the fuel system. The system that is used throughout this study is a typical fuel delivery system consisting of standard components such as a filter, pump, valve, nozzle, pipes, and two tanks. Running representative tests on a fuel system are problematic because of the time, cost, and reproduction constraints involved in capturing any significant degradation. Simulating significant degradation requires running over a considerable period; this cannot be reproduced quickly and is costly

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Fault detection and diagnosis of low delta-T syndrome in air handling unit cooling coils

In the built environment, most energy is used to promote well-being, health, and comfort. The demand for cooling will increase sharply as a result of global warming, better thermal insulation, and the heat island effect. It is therefore increasingly important that cooling installations function optimally. Currently, there are many chilled water installations that suffer from the so-called low delta-T syndrome. The return water temperature from the installations is lower than predetermined and the difference with the supply temperature is smaller, low delta-T. This has adverse consequences for the efficiency of the chillers and/or heat pump and for the energy consumption of the pumps. The result is an energy consumption that is 20-40% higher for cooling than calculated in advance. It is important to be able to detect and analyze the low-dT syndrome properly. Based on this, a software module has been developed that can use the data from a building management system to determine the low-dT syndrome and identify possible causes. Building management systems can be equipped with fault detection and diagnosis module for continuous monitoring of the performance of installations, and continuous commissioning (Cx). This would ensure that the energy consumption of the cooling installations remains as low as possible. Within the project, the first prototype of such a module was built. This will be further refined and expanded in ongoing future projects of other PDEng trainees

Proposed exergetic based leak detection and diagnosis methodology for automotive carbon dioxide air conditioning systems

Due to the overwhelming concern of global warming and ozone depletion, the replacement of many currently used refrigerants is a pressing matter within all sectors of refrigeration. Presently, the hydroflourocarbon (HFC) 134a, the working fluid of automotive air conditioning (AC) systems, greatly contributes to global warming as the result of system leakage. Both chemical and natural refrigerant losses impose threats to the environment and human health as well as reduce operational efficiency which increases energy consumption. If no action is taken to replace the chemical refrigerants, then it is proposed that the emissions from fluorinated gasses would increase from 65.2 million tons of carbon dioxide (the value found in 1995) to 98 million tons by 2010 [EurActiv.com 2004]. Natural refrigerants have gained worldwide attention as the logical replacement for chemical refrigerants. Carbon dioxide (CO2) is the natural refrigerant receiving the most attention due to its abundance in nature. When deciding to replace a refrigerant worldwide, many factors are taken under consideration. The benefits and necessary changes that occur when using CO2 as the working fluid are explored. One important aspect of using CO2 as a replacement refrigerant in automotive AC systems lies in diagnosing refrigerant leakage within a faulty system. A reliable and easy to use refrigerant leakage detection and diagnosis system is a necessity for automotive mechanics. In current research at RIT, advanced thermodynamics is being used to develop a fault detection and diagnosis system specifically for the future CO2 automotive AC systems. A simulation of the automotive air conditioning system using the software program Engineering Equation Solver (EES) is developed to simulate normal and faulty operation of the AC system. The model incorporates an exergetic analysis which combines the conservation of mass and conservation of energy laws with the second law of Thermodynamics. Fundamental laws of thermodynamics are used to verify data provided by past work [McEnaney 1999] obtained during normal operation. Using the EES model, refrigerant losses are simulated throughout the system one at a time at locations prone to leakage and the model produces a faulty operating data library. Analyzing the simulated fault data for possible trends or patterns is done in order to detect future system faults and to diagnose the faults accordingly. Trends are produced from the faulty data and are shown in graphical form. It is possible to detect and diagnose leaks by looking at the trends for a component where leaks are not even occurring

Development and Application of a Digital Twin for Chiller Plant Performance Assessment

As the complexity of industrial equipment continues to increase, the management of the individual machines and integrated operations becomes difficult without computer tools. The availability of streaming data from manufacturing floors, plant operations, and deployed fleets can be overwhelming to analyze, although it provides opportunities to improve performance. The use of dedicated monitoring systems in the plant and field to troubleshoot machinery can be integrated within a product lifecycle management (PLM) architecture to offer greater features. PLM offers virtual processes and software tools for the design, analysis, monitoring, and support of engineering systems and products. Within this paradigm, a digital twin can estimate system behavior based on the assembled physical models and the operating data for preventive maintenance efforts. PLM software can store computer-aided-design, computer-aided-engineering, advanced manufacturing, and data in cloud form for remote access. Integrating physical and performance data into a single database provides flexibility and adaptability while allowing distant commanding and health monitoring of dynamic systems. The recent attention on global warming, and the minimization of energy consumption can be partially addressed by examining those economic sectors that use large quantities of electric power. Across the United States, heating, ventilation, and air conditioning (HVAC) systems use a collective $14 Billion of resources to control the temperature of commercial and residential spaces. A typical commercial HVAC system consists of a chiller plant, water pumps for fluid circulation, multiple heat exchangers, and iii forced air blowers. In this research project, a digital twin is created for a single compressor chilled water-based HVAC system using a multi-disciplinary CAE software package. The system level models are assembled to describe a 1400 ton chiller located in the East-side chiller plant on the Clemson University (Clemson, SC) campus. The dynamic models that estimate the fluid pressures, temperatures, and flow rates, as well as the electrical and mechanical power consumption, are validated against the operating data streamed through the OptiCX System. To demonstrate the capabilities of this digital twin tool in a preventive maintenance mode, various degradations are virtually investigated in the chiller plant\u27s components. The mechanical pump efficiency, electric pump motor friction, pipe blockage, air flow rate sensor, and the expansion valve opening were degraded by 3% to 5%, which impacted component behavior and system performance. The analysis of these predicted plant signals helped to establish preventive maintenance thresholds on these components, which should promote improved plant reliability. A digital twin provides additional flexibility than stand-alone monitoring technologies due to the capability of simulating customized scenarios for analyzing failure-prone conditions and overall equipment effectiveness (OEE). The PLM-based digital twin offers a design and prognostic platform for HVAC systems

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

Abstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings

A Review of Prognostics and Health Management Applications in Nuclear Power Plants

The US operating fleet of light water reactors (LWRs) is currently undergoing life extensions from the original 40-year license to 60 years of operation. In the US, 74 reactors have been approved for the first round license extension, and 19 additional applications are currently under review. Safe and economic operation of these plants beyond 60 years is now being considered in anticipation of a second round of license extensions to 80 years of operation.Greater situational awareness of key systems, structures, and components (SSCs) can provide the technical basis for extending the life of SSCs beyond the original design life and supports improvements in both safety and economics by supporting optimized maintenance planning and power uprates. These issues are not specific to the aging LWRs; future reactors (including Generation III+ LWRs, advanced reactors, small modular reactors, and fast reactors) can benefit from the same situational awareness. In fact, many SMR and advanced reactor designs have increased operating cycles (typically four years up to forty years), which reduce the opportunities for inspection and maintenance at frequent, scheduled outages. Understanding of the current condition of key equipment and the expected evolution of degradation during the next operating cycle allows for targeted inspection and maintenance activities. This article reviews the state of the art and the state of practice of prognostics and health management (PHM) for nuclear power systems. Key research needs and technical gaps are highlighted that must be addressed in order to fully realize the benefits of PHM in nuclear facilities