Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems

Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data, none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update and upgrade, module changes, system reengineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.Comment: This research methodology has opened up new and practical applications in the software domain. In the coming decades, we can expect a significant amount of attention and practical implementation in this area worldwid

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Jätevedenpuhdistamojen prosessinohjauksen ja operoinnin kehittäminen data-analytiikan avulla: esimerkkejä teollisuudesta ja kansainvälisiltä puhdistamoilta

Instrumentation, control and automation are central for operation of municipal wastewater treatment plants. Treatment performance can be further improved and secured by processing and analyzing the collected process and equipment data. New challenges from resource efficiency, climate change and aging infrastructure increase the demand for understanding and controlling plant-wide interactions. This study aims to review what needs, barriers, incentives and opportunities Finnish wastewater treatment plants have for developing current process control and operation systems with data analytics. The study is conducted through interviews, thematic analysis and case studies of real-life applications in process industries and international utilities. Results indicate that for many utilities, additional measures for quality assurance of instruments, equipment and controllers are necessary before advanced control strategies can be applied. Readily available data could be used to improve the operational reliability of the process. 14 case studies of advanced data processing, analysis and visualization methods used in Finnish and international wastewater treatment plants as well as Finnish process industries are reviewed. Examples include process optimization and quality assurance solutions that have proven benefits in operational use. Applicability of these solutions for identified development needs is initially evaluated. Some of the examples are estimated to have direct potential for application in Finnish WWTPs. For other case studies, further piloting or research efforts to assess the feasibility and cost-benefits for WWTPs are suggested. As plant operation becomes more centralized and outsourced in the future, need for applying data analytics is expected to increase.Prosessinohjaus- ja automaatiojärjestelmillä on keskeinen rooli modernien jätevedenpuhdistamojen operoinnissa. Prosessi- ja laitetietoa paremmin hyödyntämällä prosessia voidaan ohjata entistä tehokkaammin ja luotettavammin. Kiertotalous, ilmastonmuutos ja infrastruktuurin ikääntyminen korostavat entisestään tarvetta ymmärtää ja ohjata myös eri osaprosessien välisiä vuorovaikutuksia. Tässä työssä tarkastellaan tarpeita, esteitä, kannustimia ja mahdollisuuksia kehittää jätevedenpuhdistamojen ohjausta ja operointia data-analytiikan avulla. Eri sidosryhmien näkemyksiä kartoitetaan haastatteluilla, joiden tuloksia käsitellään temaattisen analyysin kautta. Löydösten perusteella potentiaalisia ratkaisuja kartoitetaan suomalaisten ja kansainvälisten puhdistamojen sekä prosessiteollisuuden jo käyttämistä sovelluksista. Löydökset osoittavat, että monilla puhdistamoilla tarvitaan nykyistä merkittävästi kattavampia menetelmiä instrumentoinnin, laitteiston ja ohjauksen laadunvarmistukseen, ennen kuin edistyneempien prosessinohjausmenetelmien käyttöönotto on mahdollista. Operoinnin toimintavarmuutta ja luotettavuutta voitaisiin kehittää monin tavoin hyödyntämällä jo kerättyä prosessi- ja laitetietoa. Työssä esitellään yhteensä 14 esimerkkiä puhdistamoilla ja prosessiteollisuudessa käytössä olevista prosessinohjaus- ja laadunvarmistusmenetelmistä. Osalla ratkaisuista arvioidaan sellaisenaan olevan laajaa sovelluspotentiaalia suomalaisilla jätevedenpuhdistamoilla. Useiden ratkaisujen käyttöönottoa voitaisiin edistää pilotoinnilla tai jatkotutkimuksella potentiaalisten hyötyjen ja kustannusten arvioimiseksi. Jo kerättyä prosessi- ja laitetietoa hyödyntävien ratkaisujen kysynnän odotetaan tulevaisuudessa lisääntyvän, kun puhdistamojen operointi keskittyy ja paineet kustannus- ja energiatehokkuudelle kasvavat

Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions. Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models. SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value. The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis. The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies