Applied fault detection and diagnosis for industrial gas turbine systems

The paper presents readily implementable approaches for fault detection and diagnosis (FDD) based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering (HC) and self-organizing map neural networks (SOMNNs) are shown to provide robust and user-friendly tools for application to industrial gas turbine (IGT) systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps (CMs) that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world

Early Damage State Criterion from a Fault-Seeded Helicopter Gear Using Acoustic Emission and Neural Networks

In response to five failures since 2008 of the tail gearbox of multiple models of Sikorsky\u27s H-60 helicopter, acoustic emission (AE) data collected from a rotating gearbox test stand at the Naval Air Station in Patuxtent River, MD, was used to monitor the initiation and propagation of a flaw from an electro-discharge machined (EDM) notch seeded on the face of a gear tooth. A period of testing was considered which spanned ~300,000 seconds or ~83 hours and culminates to a damage state such that a flaw has initiated on both ends of the EDM notch. AE data was analyzed for three separate channels which span a wide range of amplitude thresholds using clustering methods and verification algorithms developed at the Embry-Riddle Aeronautical University (ERAU) Structure Health Monitoring (SHM) and Nondestructive Evaluation (NDE) Laboratory. Energy, duration, amplitude, and average frequency of the AE signals were input into the Kohonen Self-Organizing Map (KSOM) artificial neural network (ANN) function in NeuralWorks Professional II/Plus software to separate cracking signals from other mechanisms such as noise and plastic deformation. Visual inspection and statistical analysis of the data in the AE plots created using the output ANN results was used to separate the cluster(s) which exhibited higher amplitude and energy, and lower duration and average frequency; hits typical to cracking. The similarities and differences in the progression of clusters sourced to cracking for each of the three channels is discussed. Cumulative testing time plots of AE parameters were compiled using both entire data sets and using clusters representative of cracking mechanisms. Replica cross sections which were taken throughout testing visually display, in chronological fashion, circumferential crack growth across gear splines adjacent to the spline with the EDM notch. Data analysis techniques are used in conjunction with replica cross sections to provide insight into the AE activity for crack initiation and crack propagation and define early damage state detection criterion for rotary components

Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map

Acoustic emission nondestructive testing has been used for real-time monitoring of complex structures. All of the structures were made of materials at least 0.070 inch thick. The purpose of this research was to demonstrate the feasibility of using neural networks to classify acoustic emission signals gathered from a pressure vessel made of 2024-T3 aluminum 0.040 inches thick, i.e. thin aluminum sheet. AE waveforms were recorded during fatigue cycling of one pressure vessel using a wide band transducer and a digital oscilloscope connected to a computer. The source for each signal was determined using two narrow band transducers and a LOCAN-AT data acquisition system. The power spectrum was calculated for each waveform. A Kohonen self-organizing map (SOM) was used to cluster the spectra. The network clustered the data on a two-dimensional feature space according to the source of the signal. A total of 3,600 power spectra were used to train the neural network, and 1,800 were used to test the network. Initially there was overlap between the clusters on the two-dimensional feature space; however, this was found to be due to human error. The SOM itself correctly classified all of the signals

Microstructural Characterization of Shrouded Plasma Sprayed Titanium Coatings

Titanium and its alloys are often used for corrosion protection because they are able to offer a high chemical resistance against various corrosive media. In this paper, shrouded plasma spray technology was applied to produce titanium coatings. A solid shroud with an external shrouding gas was used to plasma spray titanium powder feedstock with aim to reduce the oxide content in the as-sprayed coatings. The titanium coatings were assessed by optical microscope, scanning electron microscopy, X-ray diffraction, LECO combustion method and Vickers microhardness testing. The results showed that the presence of the shroud and the external shrouding gas led to a dense microstructure with a low porosity in the as-prayed titanium coatings. The oxygen and nitrogen contents in the titanium coating were kept at a low level due to the shielding effect of the shroud attachment and the external shrouding gas. The dominant phase in the shrouded titanium coatings was mainly composed of α-Ti phase, which was very similar to the titanium feedstock powders. The shrouded plasma sprayed titanium coatings had a Vickers microhardness of 404.2 ±103.2 H

An investigation into the prognosis of electromagnetic relays.

Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications. Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device. Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm. Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made

Acoustic Emission Signal Classification for Gearbox Failure Detection

The purpose of this research is to develop a methodology and technique to determine the optimal number of clusters in acoustic emission (AE) data obtained from a ground test stand of a rotating H-60 helicopter tail gearbox by using mathematical algorithms and visual inspection. Signs of fatigue crack growth were observed from the AE signals acquired from the result of the optimal number of clusters in a data set. Previous researches have determined the number of clusters by visually inspecting the AE plots from number of iterations. This research is focused on finding the optimal number of clusters in the data set by using mathematical algorithms then using visual verification to confirm it. The AE data were acquired from the ground test stand that simulates the tail end of an H-60 Seahawk at Naval Air Station in Patuxant River, Maryland. The data acquired were filtered to eliminate durations that were greater than 100,000 ìs and 0 energy hit data to investigate the failure mechanisms occurring on the output bevel gear. From the filtered data, different AE signal parameters were chosen to perform iterations to see which clustering algorithms and number of outputs is the best. The clustering algorithms utilized are the Kohonen Self-organizing Map (SOM), k-mean and Gaussian Mixture Model (GMM). From the clustering iterations, the three cluster criterion algorithms were performed to observe the suggested optimal number of cluster by the criterions. The three criterion algorithms utilized are the Davies-Bouldin, Silhouette and Tou Criterions. After the criterions had suggested the optimal number of cluster for each data set, visual verification by observing the AE plots and statistical analysis of each cluster were performed. By observing the AE plots and the statistical analysis, the optimal number of cluster in the data set and effective clustering algorithms were determined. Along with the optimal number of clusters and effective clustering algorithm, the mechanisms of each cluster can be determined from the statistical analysis as well. From the results, the 5 cluster output using the Kohonen SOM clustering algorithm showed the distinct separation of clusters. Using the determined number of clusters and the effective clustering algorithms, the AE data sets were analyzed for the fatigue crack growth. Recorded data from the mid test and end test of the data acquisition period were utilized. After each set of clusters were associated with different mechanisms dependent on their AE characteristics. It was possible to detect the increase in the activities of the fatigue crack data points. This indicates that the fatigue crack is growing as the acquisition continued on the H-60 Seahawk ground test stand and that AE has a good potential for early crack detection in gearbox components