The reliability of gas turbine health state monitoring and forecasting depends on the quality of sensor measurements directly taken from the unit. Outlier detection techniques have acquired a major importance, as they are capable of removing anomalous measurements and improve data quality. To this purpose, statistical parametric methodologies are widely employed thanks to the limited knowledge of the specific unit required to perform the analysis. The backward and forward moving window (BFMW) k-σ methodology proved its effectiveness in a previous study performed by the authors, to also manage dynamic time series, i.e. during a transient. However, the estimators used by the k-σ methodology are usually characterized by low statistical robustness and resistance. This paper aims at evaluating the benefits of implementing robust statistical estimators for the BFMW framework. Three different approaches are considered in this paper. The first methodology, k-MAD, replaces mean and standard deviation of the k-σ methodology with median and mean absolute deviation (MAD), respectively. The second methodology, σ-MAD, is a novel hybrid scheme combining the k-σ and the k-MAD methodologies for the backward and the forward windows, respectively. Finally, the bi-weight methodology implements bi-weight mean and bi-weight standard deviation as location and dispersion estimators. First, the parameters of these methodologies are tuned and the respective performance is compared by means of simulated data. Different scenarios are considered to evaluate statistical efficiency, robustness and resistance. Subsequently, the performance of these methodologies is further investigated by injecting outliers in field data sets taken on selected Siemens gas turbines. Results prove that all the investigated methodologies are suitable for outlier identification. Advantages and drawbacks of each methodology allow the identification of different scenarios in which their application can be most effective.

Anomaly detection in sensor time series is a crucial aspect for raw data cleaning in gas turbine industry. In addition to efficiency, a successful methodology for industrial applications should be also characterized by ease of implementation and operation. To this purpose, a comprehensive and straightforward approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) is proposed in this paper. The tool consists of two main algorithms, i.e. the Anomaly Detection Algorithm (ADA) and the Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering based on gross physics threshold application, inter-sensor statistical analysis (sensor voting) and single-sensor statistical analysis. Anomalies in the time series are identified, by the ADA, together with their characteristics, which are analyzed by the ACA to perform their classification. Fault classes discriminate among anomalies according to their time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. Results of anomaly identification and classification can subsequently be used for sensor diagnostic purposes. The performance of the tool is assessed in this paper by analyzing two temperature time series with redundant sensors taken on a Siemens gas turbine in operation. The results show that the DICDS is able to identify and classify different types of anomalies. In particular, in the first dataset, two severely incoherent sensors are identified and their anomalies are correctly classified. In the second dataset, the DCIDS tool proves to be capable of identifying and classifying clustered spikes of different magnitudes.

Digitalization changes traditional business models by using digital technologies to improve existing offerings and to create new offerings. Current technological trends such as artificial intelligence, autonomous systems, and predictive maintenance are ideal candidate technologies to enable digitalization use cases. Often, these technologies rely on the availability of large amounts of data and the capability to process these data efficiently. In contrast to consumer markets, industrial products must fulfill higher non-functional requirements such as fast response times, 24/7 availability and stability, real-time processing, safety, or security requirements. As a consequence, processing capabilities -- ranging from multicore and manycores to even high end parallel clusters -- have to be exploited to achieve necessary performance and stability needs. In this paper, we introduce a Distributed Multicore Monitoring Framework (MoMo) which is a reference monitoring solution developed at Siemens Corporate Technology. It can be used to easily build efficient and stable diagnostic solutions which can help to understand the correctness, availability, reliability, and performance of large-scale distributed systems based on live data. Due to its small footprint MoMo can be used to analyze data directly at the data source which, for instance, can significantly reduce the network load. While MoMo's efficiency comes from the usage of multicore processors (CPUs) for running analysis in parallel, its usability is guaranteed by its capability to easily integrate with other monitoring frameworks and its usage of SPL - a domain-specific language which allows user to easily define diagnostic algorithms.

Statistical parametric methodologies are widely employed in the analysis of time series of gas turbine sensor readings. These methodologies identify outliers as a consequence of excessive deviation from a statistically-based model, derived from available observations. Among parametric techniques, the k-σ methodology demonstrates its effectiveness in the analysis of stationary time series. Furthermore, the simplicity and the clarity of this approach justify its direct application to industry. On the other hand, the k-σ methodology usually proves to be unable to adapt to dynamic time series, since it identifies observations in a transient as outliers. As this limitation is caused by the nature of the methodology itself, two improved approaches are considered in this paper in addition to the standard k-σ methodology. The two proposed methodologies maintain the same rejection rule of the standard k-σ methodology, but differ in the portions of the time series from which statistical parameters (mean and standard deviation) are inferred. The first approach performs statistical inference by considering all observations prior to the current one, which are assumed reliable, plus a forward window containing a specified number of future observations. The second approach proposed in this paper is based on a moving window scheme. Simulated data are used to tune the parameters of the proposed improved methodologies and to prove their effectiveness in adapting to dynamic time series. The moving window approach is found to be the best on simulated data in terms of True Positive Rate (TPR), False Negative Rate (FNR) and False Positive Rate (FPR). Therefore, the performance of the moving window approach is further assessed towards both different simulated scenarios and field data taken on a gas turbine.

Optimizing sophisticated PDE-based filtering methods, such as the Anisotropic Nonlinear Diffusion (AND), to GPUs is complicated and time consuming. In this work, we expressed AND as iterative multiple 3D-stencils, where each 3D-stencil is implemented into one kernel, and then we analyzed all possible kernel fusions on the GPU. We experimentally found that fusing dependent stencils with similar concurrency and lower on-chip pressure makes the optimal combination run 1, 52× faster than the next better one.

Flow simulations around building infrastructure models involve large scale complex geometries, which when discretized in adequate detail entail high computational cost. Moreover, tasks such as simulation insight by steering or optimization require many such costly simulations. In this paper, we illustrate the whole pipeline of an integrated solution for interactive computational steering, developed for complex flow simulation scenarios that depend on a moderate number of both geometric and physical parameters. A mesh generator takes building information model input data and outputs a valid cartesian discretization. A sparse-grids-based surrogate model—a less costly substitute for the parameterized simulation—uses precomputed data to deliver approximated simulation results at interactive rates. Furthermore, a distributed multi-display visualization environment shows building infrastructure together with flow data. The focus is set on scalability and intuitive user interaction.

Given the existing heterogeneous processor landscape dominated by CPUs and GPUs, topics such as programming productivity and performance portability have become increasingly important. In this context, an important question refers to how can we develop optimization strategies that cover both CPUs and GPUs. We answer this for fastsg, a library that provides functionality for handling efficiently high-dimensional functions. As it can be employed for compressing and decompressing large-scale simulation data, it finds itself at the core of a computational steering application which serves us as test case. We describe our experience with implementing fastsg's time critical routines for Intel CPUs and Nvidia Fermi GPUs. We show the differences and especially the similarities between our optimization strategies for the two architectures. With regard to our test case for which achieving high speedups is a "must'" for real-time visualization, we report a speedup of up to 6.2x times compared to the state-of-the-art implementation of the sparse grid technique for GPUs.

Multi-core parallelism and accelerators are becoming common features of today's computer systems, as they allow for computational power without sacrificing energy efficiency. Due to heterogeneity, tuning for each type of compute unit and adequate load balancing is essential. This paper proposes static and dynamic solutions for load balancing in the context of an application for visualizing high-dimensional simulation data. The application relies on the sparse grid technique for data compression. Its performance critical part is the interpolation routine used for decompression. Results show that our load balancing scheme allows for an efficient acceleration of interpolation on heterogeneous systems containing multi-core CPUs and GPUs.

Future space applications will demand for architectures with High Performance Computing (HPC) capabilities. In this scope, on-board computer designers will have to select between different technologies and designs, the most reliable and most efficient ones in terms of performance and power consumption. In this paper, we investigate the behavior of an Image Reconstruction Algorithm on high performance multi-core CPUs and many-core GPUs. It turns out that SAR applications can profit much more from the architecture and the capabilities of many-core GPUs than from modern multi-core CPUs. We give some remarks on how these types of HPC components can be integrated on future space-based on-board computing platforms. Throughout the paper, we illustrate by comparison the advantages and disadvantages of using GPUs over CPUs for SAR Applications. Other than this, we explain the programming and parallelization paradigms applied to the SAR application to increase its performance and efficiency on CPUs and GPUs respectively.

The name sparse grids denotes a highly space-efficient, grid-based numerical technique to approximate high-dimensional functions. Although employed in a broad spectrum of applications from different fields, there have only been few tries to use it in real time visualization (e.g. [1]), due to complex data structures and long algorithm runtime. In this work we present a novel approach inspired by principles of I/0-efficient algorithms. Locally applied coefficient permutations lead to improved cache performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations on modern multi-core systems by a factor of 37 for a grid size of 127 million points. For larger problems the speedup is even increasing, and with execution times below 1 s, sparse grids are well-suited for visualization applications. Furthermore, we point out how a broad class of sparse grid algorithms can benefit from our approach.

In a complex processor landscape dominated by multi-and many-core processors, simplifying programming plays a crucial role in enhancing developers’ productivity. One way is to use highly tuned library functions. In this paper we present fastsg, an optimized library for the sparse grid technique with support for dimensional truncation. With optimizations for best cache use and vectorization, we improve the performance on one processor core up to a factor of 10. Parallelization using OpenMP scales almost linearly on a 12-core system.

The well-known power wall resulting in multi-cores requires special techniques for speeding up applications. In this sense, parallelization plays a crucial role. Besides standard serial optimizations, techniques such as input specialization can also bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation is an inherently hierarchical method of interpolation employed for example in computational steering applications for decompressing high-dimensional simulation data. In this context, improving the speedup is essential for real-time visualization. Using input specialization, we report a speedup of up to 9x over the non-specialized version. The paper covers the steps we took to reach this speedup by means of input adaptivity. Our algorithms will be integrated in fastsg, a library for fast sparse grid interpolation.

The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid algorithms for our application, on Nvidia GPUs. The main idea consists of a bijective mapping between the set of points in a multi-dimensional sparse grid and a set of consecutive natural numbers. The resulting data structure consumes a minimum amount of memory. For a 10-dimensional sparse grid with approximately 127 million points, it consumes up to 30 times less memory than trees or hash tables which are typically used. Compared to a sequential CPU implementation, the speedups achieved on GPU are up to 17 for compression and up to 70 for decompression, respectively. We show that the optimizations are also applicable to multicore CPUs.

With the emergence of multi-core CPUs, parallel computing has made the leap from being a paradigm mainly used in high performance computing to imposing itself as one of the standards used in mainstream computing. The field of video compression and decompression naturally embraces parallel computing since video compression is a computationally intensive task that can be successfully distributed among two or more computing cores. This paper describes a different approach to video compression based on image vectorization on the novel Cell/B.E. architecture. Video frames are analyzed and interesting features such as edges, corners and patches are extracted, with significant performance speedups obtained on the Cell processor and maintaining good image quality. Finally, the features and the topological relations between them are used to reconstruct the original image.

Although the landscape of mobile devices is continuously evolving towards more powerful terminals, the limitations regarding processing power, memory capacity and speed, network connection and battery, are still present and at certain loads fail to satisfy the requirements of complex applications. Being always connected to the network offers a solution to this problem as the server infrastructure of the service provider can substitute part of the local functionality whenever the terminal becomes overloaded. Automatic reconfigurability refers to the ability to automatically switch between local and remote services in a seamless manner whenever the conditions impose it, both at start time and run time. This paper describes the requirements and the design of a context aware middleware solution that manages the services invoked by applications. It also presents the specific programming model including the service lifecycle that enables centralized service control and efficient migration of the service state.

The convergence of telecommunication and Web 2.0 services is leading to new opportunities for the telecommunications market. Companies are looking for ways to include their services in Web 2.0 applications. Predictions suggest that future telecommunication networks will be based on the IP Multimedia Subsystem (IMS), an all IP telecommunication core network. This paper describes an approach to combining Web 2.0 enabled applications, namely widgets, with telecommunication features using IMS. Widgets are small applications based on Web technologies that run on the client device. A new abstraction layer with interfaces for the different telecommunication features will be introduced. In addition a widget engine that makes these telecommunication interfaces available to its widgets will be presented. This will allow the rapid development of IMS applications for external developers and the combination of other Web 2.0 services with IMS features.

In this paper we provide the description of a new mobility service for an IP Multimedia Subsystem (IMS) platform. It optimizes the operation of multimedia services in a wireless heterogeneous environment containing multiple access networks with different cost per bandwidth ratio. It extends the SIP Presence Service with mobility related information, i.e. Media Independent Handover (MIH). The terminals are able to exchange specific handover knowledge with the remote service provisioning infrastructure and to receive commands adapted to the profile of all the parties involved in multimedia sessions, transparent to the actual multimedia service. The system was designed to enable the adaptation of multimedia sessions to the profile of the users and to the momentary network conditions on an end-to-end basis.

The high popularity of mobile computing and the increasing number of powerful mobile phones that are able to access high speed data networks pave the way to new scenarios for multimedia applications for mobile devices. Older limitations on network access, displaying capabilities and computing power have been surmounted. The users now have powerful devices and more bandwidth and expect that new, as well as traditional, multimedia applications are available on them. There is a need to offer users a coherent experience and provide multimedia independent of the constrains of the device in which it runs, and the network access technologies used. A way to achieve this is to use a software framework on top of which applications can be built. This framework is able to run on top of a wide variety of mobile platforms and enables a fast and easy deployment of multimedia clients. This paper analyzes the requirements and describes the architecture of Multimedia Open Internet Services and Telecommunication Environment (MONSTER), a framework for mobile multimedia clients currently developed at the Fraunhofer Institute for Open Communication Systems (FOKUS).