Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE

Failure monitoring in dynamic systems: Model construction without fault training data

Advances in the use of autoregressive models, pattern recognition methods, and hidden Markov models for on-line health monitoring of dynamic systems (such as DSN antennas) have recently been reported. However, the algorithms described in previous work have the significant drawback that data acquired under fault conditions are assumed to be available in order to train the model used for monitoring the system under observation. This article reports that this assumption can be relaxed and that hidden Markov monitoring models can be constructed using only data acquired under normal conditions and prior knowledge of the system characteristics being measured. The method is described and evaluated on data from the DSS 13 34-m beam wave guide antenna. The primary conclusion from the experimental results is that the method is indeed practical and holds considerable promise for application at the 70-m antenna sites where acquisition of fault data under controlled conditions is not realistic

Fault Detection of Gearbox from Inverter Signals Using Advanced Signal Processing Techniques

The gear faults are time-localized transient events so time-frequency analysis techniques (such as the Short-Time Fourier Transform, Wavelet Transform, motor current signature analysis) are widely used to deal with non-stationary and nonlinear signals. Newly developed signal processing techniques (such as empirical mode decomposition and Teager Kaiser Energy Operator) enabled the recognition of the vibration modes that coexist in the system, and to have a better understanding of the nature of the fault information contained in the vibration signal. However these methods require a lot of computational power so this paper presents a novel approach of gearbox fault detection using the inverter signals to monitor the load, rather than the motor current. The proposed technique could be used for continuous monitoring as well as on-line damage detection systems for gearbox maintenance

Power distribution system fault monitoring device for supply networks in Nigeria

Electric power is the bedrock of our modern way of life. In Nigeria, power supply availability, sufficiency and reliability are major operational challenges. At the generation and transmission level, effort is made to ensure status monitoring and fault detection on the power network, but at the distribution level, particularly within domestic consumer communities there are no fault monitoring and detection devices except for HRC fuses at the feeder pillar. Unfortunately, these fuses are sometimes replaced by a copper wire bridge at some locations rendering the system unprotected and creating a great potential for transformer destruction on overload. This study is focused on designing an on-site power system monitoring device to be deployed on selected household entry power cables for detecting and indicating when phase off, low voltage, high voltage, over current, and blown fuse occurs on the building’s incomer line. The fault indication will help in reducing troubleshooting time and also ensure quick service restoration. After design implementation, the test result confirms design accuracy, device functionality and suitability as a low-cost solution to power supply system fault monitoring within local communities

Real-time diagnostics for a reusable rocket engine

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2

AUTOMATED DIESEL ENGINE CONDITION & PERFORMANCE MONITORING & THE APPLICATION OF NEURAL NETWORKS TO FAULT DIAGNOSIS

The overall aim of this research was to design, configure and validate a system which was capable of on-line performance monitoring and fault diagnosis of a diesel engine. This thesis details the development and evaluation of a comprehensive engine test facility and automated engine performance monitoring package. Results of a diesel engine fault study were used to ascertain commonly occurring faults and their realistic severities are discussed. The research shows how computer simulation and rig testing can be applied to validate the effects of faults on engine performance and quantify fault severities. A substantial amount of engine test work has been conducted to investigate the effects of various faults on high speed diesel engine performance. A detailed analysis of the engine test data has led to the development of explicit fault-symptom relationships and the identification of key sensors that may be fitted to a diesel engine for diagnostic purposes. The application of a neural network based approach to diesel engine fault diagnosis has been investigated. This work has included an assessment of neural network performance at engine torques and speeds where it was not trained, noisy engine data, faulty sensor data, varying fault severities and novel faults which were similar to those which the network had been trained on. The work has shown that diagnosis using raw neural network outputs under operational conditions would be inadequate. To overcome these inadequacies a new technique using an on-line diagnostic database incorporating 'weight adjusting' and 'confidence factor' algorithms has been developed and validated. The results show a neural network combined with an on-line diagnostic database can be successfully used for practical diesel engine fault diagnosis to offer a realistic alternative to current fault diagnosis techniques.The Ministry Of Defenc

Data validation: a case study for a feed-drive monitoring

The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of requests on control, reliability, availability of machine-tools and on work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring, … The improvement of monitoring of the main machine-tools devices, the feed-drives and the spindles that drive the cutting process, can be realised by estimating their fault sensitive physical parameters from their continuous-time model. We have chosen to use a continuous-time ARX model. We particularly focus on slow time varying phenomena. This estimation should run while there is no machining process to avoid false detection of faults on the machine due to the cutting process. High speed motions, that occur at least for each tool exchange, are exploited. Some functional constraints require the use of an off-line estimation method, we have chosen an ordinary least squares method. Estimating the physical parameters is insufficient to obtain an efficient monitoring. A measurement analysis and validation are necessary as the validation of the estimated physical parameters. An approach of the measurement and physical parameter estimation validation for a NC machine-tool feed-drive is proposed