This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations.

One of Bosch Rexroth’s newest developments is the ABPAC power unit, which is both modular and configurable. The modular design of the ABPAC is enhanced by a selfcontained Condition Monitoring System (CMS), which can also be used to retrofit existing designs. This dissertation shows how Industry 4.0-Technology provides special advantages for the diverse user profiles. Today, Hydraulic Power Units have either scheduled intervals for preventive maintenance or are repaired in case of component failures. Preventive maintenance concepts, until now, did not fully utilize the entire life expectancy of the components, causing higher maintenance costs and prolonged downtimes. Risk of unscheduled downtime forces the customer to stock an array of spare parts leading to higher inventory costs or in the event a spare is not readily available, the customer may encounter long delivery times and extended downtime. Bearing this in mind, we’ve conceived the idea of a self-contained intelligent Condition Monitoring System including a predictive maintenance concept, which is explained in the following.

<p>Condition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and achieving a more robust machining process. Failures in the machining process and machine tool components may also have negative effects on the final produced part. Instabilities in machining processes also shortens the life time of the cutting edges and machine tool.</p><p>The condition monitoring system may utilise information from several sources to facilitate the detection of instabilities in the machining process. To avoid additional complexity to the machining system the use of internal sensors is considered. The focus in this thesis has been to investigate if information related to the machining process can be extracted directly from the internal sensors of the machine tool.</p><p>The main contibutions of this work is a further understanding of the direct response from both linear and angular position encoders due the variations in the machining process. The analysis of the response from unbalance testing of turn tables and two types of milling processes, i.e. disc-milling and slot-milling, is presented. It is shown that operational frequencies, such as cutter frequency and tooth-passing frequency, can be extracted from both active and inactive machine axes, but the response from an active machine axis involves a more complex analysis. Various methods for the analysis of the responses in time domain, frequency domain and phase space are presented.</p> / QC 20100518

Slow speed rotating machines are the mainstay of several industrial applications worldwide. They can be found in paper and steel mills, rotating biological contractors, wind turbines etc. Operational experience of such machinery has not only revealed the early design problems but has also presented opportunities for further significant improvements in the technology and economics of the machines. Slow speed rotating machinery maintenance, mostly related to bearings, shafts and gearbox problems, represents the cause of extended outages. Rotating machinery components such as gearboxes, shafts and bearings degrade slowly with operating time. Such a slow degradation process can be identified if a robust on-line monitoring and predictive maintenance technology is used to detect impending problems and allow repairs to be scheduled. To keep machines functioning at optimal levels, failure detection of such vital components is important as any mechanical degradation or wear, if is not impeded in time, will often progress to more serious damage affecting the operational performance of the machine. This requires far more costly repairs than simply replacing a part. Over the last few years there have been many developments in the use of Acoustic Emission (AE) technology and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on slow speed rotating machinery. Unlike conventional technologies such as thermography, oil analysis, strain measurements and vibration, AE has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This programme of research involves laboratory tests for monitoring slow speed rotating machinery components (shafts and bearings) using AE technology. To implement this objective, two test rigs have been designed to assess the capability of AE as an effective tool for detection of incipient defects within low speed machine components (e.g. shafts and bearings). The focus of the experimental work will be on the initiation and growth of natural defects. Further, this research work investigates the source characterizations of AE signals associated with such bearings whilst in operation. It is also hoped that at the end of this research program, a reliable on-line monitoring scheme used for slow speed rotating machinery components can be developed.

Unmanned Air Vehicles (UAVs) are becoming more widely used in both military and civilian applications. Some of the largest UAVs have power systems equivalent to that of a military strike jet making power management an important aspect of their design. As they have developed, the amount of power needed for loads has increased. This has placed increase strain on the on-board generators and a need for higher reliability. In normal operation these generators are sized to be able to power all on-board systems with out overheating. Under abnormal operating conditions these generators may start to overheat, causing the loss of the generator's power output. The research presented here aims to answer two main questions: 1) Is it possible to predict when an overheat fault will occur based on the expected power usage defined by mission profiles? 2) Can an overheat fault be prevented while still allowing power to be distributed to necessary loads to allow mission completion? This is achieved by a load management algorithm, which adjusts the load profile for a mission, by either displacing the load to spare generators, or resting the generator to cool it down. The result is that for non-catastrophic faults the faulty generator does not need to be fully shut down and missions can continue rather than having to be aborted. This thesis presents the development of the load management system including the algorithm, prediction method and the models used for prediction. Ultimately, the algorithms developed are tested on a generator test rig. The main contribution of this work is the design of a prognostic load management algorithm. Secondary contributions are the use of a lumped parameter thermal model within a condition monitoring application, and the creation of a system identification model to describe the thermal dynamics of a generator.

Då komponenter för tillståndsövervakning idag är billiga, små storleksmässigt och kraftfullare än tidigare, kan hårdvara byggas ihop, mjukvara programmeras och sedan appliceras på kritiska delar i ett system, mer kostnadseffektivt och i större omfattning än tidigare. I stor skala kallas detta sakernas internet, och är framtiden för underhållsarbete då personal inte längre behöver vara fysiskt närvarande i samma grad som tidigare. En proprietär lösning kostar idag vanligtvis över 5 000 kr. Detta projekt har behandlat prototyper till en kostnad av cirka 1 000 kr med öppen hårdvara och mjukvara, vilket stödjer affärsutveckling och bidrar till ett bredare spektra av leverantörer, vilket sätter press på marknaden gällande olika lösningar. Syftet med denna studie är att utveckla och testa mätningar i verklig miljö för tillståndsövervakning av utsatta delar av järnvägsinfrastrukturen, som punktfel i spårläge och spårväxlar. Projektet innefattar prototyper, energihantering, loggning av data och hur användbara dessa data är. Sensorerna är av typen MEMS accelerometrar och olika montage av dessa har testats. Målsättningen har varit att utvärdera hur dessa fungerar i verklig miljö och hur användarvänliga dessa är för att mäta rörelse av räls och sliper. Mer specifikt, avser detta fältprov av accelerometer för uppvakning och energihantering, sensor för insamling av vibrationer för rörelse i spår och analys av insamlad mätdata. Fälttest visade att en accelerometerbaserad uppvakningssensor kan väcka ett mätsystem genom vibrationer i rälsen ca: 70 meter innan tåget kommer fram till sensorn. Tydligaste mätdata för analys erhölls vid montage på slipers samt då ett avstånd på ca: 70 meter togs. Montering i direkt närhet till mätobjektet på rälslivet bidrog till en signal med inslag av högfrekventa vibrationer. Då tydlig mätdata erhölls kunde antal boogies och axlar identifieras vilket kunde verifieras med bild på loket. Mätdata som analyserades kunde via integration erhålla förskjutning i vertikal samt lateral riktning. Detta är användbart, både för infrastrukturförvaltare samt för underhållsentreprenörer, då degradering kan upptäckas i god tid och förebyggande underhållsåtgärder kan sättas in mot berörda feltyper. Den önskade livslängden enligt infrastrukturförvaltare var minst fem år, och efter mätning av strömåtgång enligt nuvarande specifikation så uppnås detta om mätningar sker sju gånger dagligen med litiumbatterier (1200 mAh) som strömkälla. Som ett första steg mot sakernas internet så har detta arbete skapat en god grund för att förverkliga detta. Fortsatt arbete efter detta projekt kan innefatta gprs och Wi-Fi för internetuppkoppling samt strömmätning för att se hur mycket förbrukningen ökar. Parallellkoppling av batterier kan vara en lösning för längre livslängd ifall förbrukningen påverkas markant. Olika varianter av filtrering för en tydligare signal kan också vara av intresse. Andra typer av sensorer, både för verifiering av resultat i denna rapport samt för att utprova alternativ. Detta kan innefatta geofoner, multi-depth deflectometers och andra typer av accelerometrar. / Since condition monitoring devices today are cheap, small size and more powerful than before, hardware can be built together, software programmed, and then applied to critical parts of a system, more cost-effective and to a greater extent than before. On a large scale this is called the Internet of Things, and is the future of today's maintenance work, as staff no longer needs to be physically present to the same extent as before. A proprietary solution today generally costs over 5,000 SEK. This project has processed prototypes at a cost of approximately 1,000 SEK with open hardware and software, which supports business development and contributes to a wider range of suppliers, which puts pressure on the market for different solutions. The purpose of this study is to develop and test measurements in real environment for condition monitoring of exposed parts of the railway infrastructure, such as point errors in track and railroad switches. The project includes prototypes, energy management, data logging and how useful these data are. The sensors are of the type MEMS accelerometers and various assemblies of these have been tested. The goal has been to evaluate how these works in a real environment and how user friendly these are to measure the movement of rails and grinders. Field test showed that an accelerometer-based wake-up sensor can wake a measuring system by vibration in the rail approximately 70 meters before the train reaches the sensor. Clearest measurement data for analysis was obtained when mounted on grinders and when a distance of about 70 meters was taken. Mounting in close proximity to the measurement object on the rail life contributed to a signal with high frequency vibration input. When clear measurement data was obtained, the number of boogies and axes could be identified, this could be verified by image on the train. Measurement data analyzed could through integration obtain displacement in vertical as well as lateral direction. This is useful, both for infrastructure managers and for maintenance entrepreneurs, as degradation can be detected in time and preventive maintenance actions can be set against the relevant failure types. The desired lifespan according to infrastructure managers was at least five years, and after measuring current consumption according to the current specification, this is achieved if measurements take place seven times a day with lithium batteries (1200 mAh) as the power source. As a first step towards the Internet of Things, this work has created a good foundation to make this reality. Continued work after this project may include gprs and Wi-Fi for internet connection as well as current measurement to see how much usage is increasing. Parallel coupling of batteries can be a solution for longer service life if consumption is significantly affected. Different variants of filtering for a clearer signal may also be of interest. Other types of sensors, both for verification of results in this report and for testing alternatives. This may include geophones, multi-depth deflectometers and other types of accelerometers

Increased competition in the capital-intensive paper industry makes optimized operation to an important part of corporate strategies to reduce them overall costs. Running the machines in a reliable way contributes to higher plant availability, which is particularly important in industries where the production rate is high and a break lead to costly production downtime. To achieve high plant availability, an effective maintenance of manufacturing equipment is required. This requires that the right action is taken at the right time. The purpose of this project was to increase understanding of how operation parameters for a paper machine affect the condition of the machine. The condition is assessed by studying the vibration levels using a condition monitoring system installed on the wire section rollers. The goal was to identify tools that can help operators to run the paper machine in a harmless and cost-effective way. The study was conducted by varying three parameters: wire speed, wire tension and vacuum in the suction boxes of the wire section. Wire speed and wire tension were varied for the flat wire and the upper wire. The vacuum was only varied for the flat wire. Each parameter was varied separately and the influence on the rolls vibration levels were analyzed. The range for the different operating parameters was determined in consultation with the operating personnel. There were no clear trends of how the operation parameters affect the machine condition. However, changes in vibration levels for single rolls were found. For the flat wire the changes in vibration levels were small except for a specific roll where the vibration level dropped drastically at increased speed, increased vacuum and higher tension. Even for the upper wire the changes in vibration levels were marginal except for a specific roll, which increased tension resulted in reduced vibration level, -from 2,74mm / s to 1.56 mm / s, which is a significant difference when the alarm limit of the machine is 2.5mm / s. This is an important discovery because the rollers can be seen as vital components in a series of linked systems and their operation is necessary for the paper machine to perform its required function. A conclusion is that relatively small adjustments of operation parameters affect vibration levels, which will have an effect on the component remaining life, and hence the system availability. / Ökad konkurrens inom den kapitalintensiva pappersindustrin gör att en optimerad drift blir en viktig del i företagens strategier för att minska dem totala kostnaderna. Att köra maskinerna på ett driftsäkert sätt bidrar till högre anläggningstillgänglighet, vilket är särskilt viktigt vid industrier där produktionstakten är hög och ett avbrott leder till kostsamma produktionsbortfall. För att uppnå hög anläggningstillgänglighet krävs också ett effektivt underhåll av tillverkningsutrustningen. Detta kräver att rätt åtgärder sätts in i rätt tid. Syftet med detta examensarbete var att öka förståelsen för hur olika driftparametrar för en pappersmaskin påverkar tillståndet för maskinen. Tillståndet bedömdes genom att studera vibrationsnivåer från ett lagerövervakningssystem installerat på virapartiets valsar. Målet var att hitta redskap som kan hjälpa operatörerna att köra pappersmaskinen på ett så skonsamt och kostnadseffektivt sätt som möjligt. Studien genomfördes genom att variera tre driftparametrar: virahastighet, viraspänning och undertryck i virapartiets suglådor. Virahastighet och viraspänning varierades för både planviran och överviran. Undertryck i virapartiets suglådor varierades endast för planviran. Varje parameter varierades separat och parametrarnas påverkan på valsarnas vibrationsnivåer analyserades. Intervallet för de olika driftparametrarna bestämdes i samråd med driftspersonalen. Sett för hela maskinen gick det inte att se några tydliga trender för hur driftparametrarna påverkade tillståndet för maskinen. Däremot kunde man se förändringar i vibrationsnivå för enstaka valsar. För planviran var ändringarna i vibrationsnivåerna marginella undantaget en specifik vals där vibrationsnivån sjönk drastiskt vid ökad hastighet, ökat vacuum samt ökad spänning. Även för överviran var ändringarna i vibrationsnivåer marginella utom för en specifik vals, där ökad spänning gav minskad vibrationsnivå, -från 2,74mm/s till 1,56 mm/s vilket är en betydande skillnad då larmgränsen från maskintillverkaren är 2,5mm/s. Detta är en viktig upptäckt eftersom valsarna kan ses som vitala komponenter i ett seriekopplat system och deras funktion är nödvändig för att pappersmaskinen skall kunna utföra krävd funktion. En slutsats är att det med ganska små justeringar i driftparametrar går att påverka vibrationsnivåerna, som i sin tur påverkar komponenternas livslängd och systemets tillgänglighet.

M. Tech. (Mechanical Engineering) Vaal University of Technology / Monitoring the condition of equipment in industry is very important to prevent unplanned breakdowns and to prolong their life. This is necessary, since it is not always economically viable to stop equipment at regular intervals to do maintenance. Failure on machines can lead to high repair costs and production losses. It is thus of paramount importance that early failure symptoms be identified by means of condition monitoring. This study in the field of condition monitoring is performed to determine if the mechanical shock values induced in defect bearings could be used to measure the condition of a bearing while operating under variable speed and variable load. Variable speed and variable load is becoming more popular in industry because variable speed drives applications ensure effective process control. Variable speed application, cause fault frequencies to fluctuate and therefore vibration applications for constant speed applications, which are speed-dependent, can no longer apply. Vibration-monitoring techniques that have applied for many years have now become obsolete in these variable speed applications. Methods such as Short Time Fourier Transformation (STFT), time scale like wavelet transform, and Order tracking has been applied in variable speed applications with some success. These methods analyses the vibration phases on the signal buy compensating for the speed changes. In this thesis, the Shock pulse method is selected as the analyses tool to measure the mechanical shock. Shock pulse monitoring does not focus on the vibration phases but measures in a small-time window when mechanical shocks are induced in the bearing material before the vibration phase. There is very little documented research in the field of mechanical shock pulse monitoring for conditions of variable speed and variable loads, and therefore this research focuses on recording these mechanical shock values by empirical tests. The tests were performed on a bearing with an induced defect on the outer race. The rolling element of the bearing strikes the defect and the mechanical shock value (dBsv) is measured. The mechanical shock is measured with the Shock pulse method in a small-time window before vibration occurs. In this time window, the dBsv is recorded over time to provide diagnostic information of the bearing during acceleration, deceleration and various loading conditions. These mechanical shocks are elastic waves that mirror the impact-contact-force's time function and the Shock pulse monitoring accelerometer, which is tuned to 32 kHz, will respond to the elastic wave fronts with transient amplitudes proportional to the square of the impact velocities. The mechanical shock values were analysed and reoccurring fault levels were identified on each empirical test. These recurring events from the empirical tests were used as primary data for analysis in this research. These tests were performed on a bearing with an induced failure and it was found that the dBsv measured over time could not be used to monitor the condition of the bearing under variable speed applications. This was because the dBsv changed as the speed increased. To overcome this problem Sohoel’s theory was applied and the initial mechanical shock value (dBi) was calculated for the bearing. The dbi value was subtracted from the dBsv and a value called the maximum mechanical shock value (dBm) was obtained. The dBm values stayed constant for the duration of the test and this allowed the condition of the bearing to be measured under variable speed and variable load conditions with some exception. The exception to the findings was that the dBm values stayed constant during acceleration phases, but during the deceleration phases the values were erratic and scattered. At speed below 200rpm the dBm values did not stay constant and therefore it was concluded that the dBm value recorded the best results only when thrust on the bearing was maximum. The other exception was under no-load conditions. The values were erratic and scattered, and therefore the results were not a true reflection of the bearing condition. The third exception was that the results on bearings with various loads remained constant during increased load changes unless the loading was erratic. During erratic load changes, the results were affected. The results also indicated that the larger the defect on the bearing raceway, the higher the dBm values were. Multipil defects on the bearing race ways were not part of this thesis and this gives an opertunity for futher research. The Shock pulse monitoring technique was 100% successful in monitoring the bearing condition only while the speed of the bearing was increasing. The results obtained in this work demonstrated that the condition of bearings can be monitored in applications of variable speed and variable load if the exception are eliminated and to obtain conclusive results the mechanical shock pulses should be measured over time and not be used as once-off value.

The hydro power industry stands for new challenges due to a more fluctuating production fromwind and solar power. This requires more regulation of the production in the hydro powerstations, which increases maintenance demands. An oil leakage has not only consequencessuch as downtimes and maintenance costs, but also an environmental impact. Skellefte ̊aKraft is working towards reaching a condition based maintenance. Therefore, the purpose ofthis master thesis is to develop a model using a feedforward neural network to predict the oillevel in the control system of a Kaplan turbine and map which sensor signals that are required.The thesis will cover data from two hydro power stations, Grytfors and B ̊atfors, each ofwhich has two units, G1 and G2. Due to limitations of the database Skellefte ̊a Kraft areusing, the data has minute resolution and covers two months, December and January. Themodel is developed in MATLAB using their Deep Learning toolbox and the neural networkfeedforwardnet. Before training and testing the model, an optimization was done. Grytforshas a full range of sensor signals while B ̊atfors has half the amount and therefore, the datafor Grytfors was used in the optimization. A grid search was done to optimize the hyperpa-rameters using cross validation. To map which input parameters that are required a featureselection was done.From the result of the feature selection, power, accumulator levels 1 and 2 and pressurewere chosen as the input parameters for Grytfors. For B ̊atfors, all of the the existing sensorsignals were used instead. The model is then trained and tested for the two different powerstations. For Grytfors, the predicted oil level follows the pattern of the real oil level but thetest error is around 15-20 liter. Four different tests were done for B ̊atfors. The two firstfor unit 1, the third for unit 2 and the fourth on both units to investigate the potential of ageneral model for one power station. For B ̊atfors, the first two tests have test errors of around4-6 liters. The third and fourth tests have test errors of around 1.5 liter. In the first twotests, the December data contains a potential refill sequence and in the third test, for unit2, the data contains start and stop sequences. The results showed the importance of havingcomprehensive training data.

It is widely acknowledged that bearing failures are the primary reason for breakdowns in rotating machinery. These failures are extremely costly, particularly in terms of lost production. Roller bearings are widely used in industrial machinery and need to be maintained in good condition to ensure the continuing efficiency, effectiveness, and profitability of the production process. The research presented here is an investigation of the use of acoustic emission (AE) to monitor bearing conditions at low speeds. Many machines, particularly large, expensive machines operate at speeds below 100 rpm, and such machines are important to the industry. However, the overwhelming proportion of studies have investigated the use of AE techniques for condition monitoring of higher-speed machines (typically several hundred rpm, or even higher). Few researchers have investigated the application of these techniques to low-speed machines ( < 100 rpm), This PhD addressed this omission and has established which, of the available, AE techniques are suitable for the detection of incipient faults and measurement of fault growth in low-speed bearings. The first objective of this research program was to assess the applicability of AE techniques to monitor low-speed bearings. It was found that the measured statistical parameters successfully monitored bearing conditions at low speeds (10-100 rpm). The second objective was to identify which commonly used statistical parameters derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify the onset of a fault in either race. It was found that the change in AE amplitude and AE RMS could identify the presence of a small fault seeded into either the inner or the outer races. However, the severe attenuation of the signal from the inner race meant that, while AE amplitude and RMS could readily identify the incipient fault, kurtosis and the AE counts could not. Thus, more attention needs to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of severity of the fault. However, once the defect was established, it was found that of the parameters used only AE RMS was sensitive to defect size. The fourth objective was to assess whether the AE signal is able to detect defects located at either the centre or edge of the outer race of a bearing rotating at low speeds. It is found that all the measured AE parameters had higher values when the defect was seeded in the middle of the outer race, possibly due to the shorter path traversed by the signal between source and sensor which gave a lower attenuation than when the defect was on the edge of the outer race. Moreover, AE can detect the defect at both locations, which confirmed the applicability of the AE to monitor the defects at any location on the outer race.