<p>I denna rapport behandlas verktyget FMEA (Failure Mode Effect Analysis), dess användning och genomförande på identifierade komponenter som ingår i elmotorer tillverkade hos ABB LV Motors i Västerås. Målet med detta arbete har varit att ge lärdom om metoden FMEA på företaget för att möjliggöra användandet av verktyget bland medlemmarna på företaget och att vi implementerar verktyget på befintlig motor för att analysera de felsätt som kan uppstå. Eftersom ABB just nu har ett pågående projekt med en ny motor kan våra slutgiltiga felanalyser även ge dem information om vilka ändringar och åtgärder som kan vara till nytta vid utformningen av den nya motorn.</p><p>Efter två veckors praktik i verkstan och en grundlig genomgång av företagets reklamationer kunde vi avgränsa arbetet genom att identifiera de komponenter som analyserna skulle genomföras på. Efter en genomgång tillsammans med våra handledare på företaget kom vi fram till att utföra våra analyser på fem komponenter, två av dem framstod som mycket kritiska som upptäcktes redan vid genomgång av reklamationerna.</p><p>Komponenterna som vi har analyserat är:</p><p>1. Lager</p><p>2. Lindning</p><p>3. Tätning av uttagslåda och uttagslock</p><p>4. Fötter</p><p>5. Sköldar</p><p>Lager som är den näst största kritiska komponent som vi tittat på är en del i motorn som är väldigt känslig, det är viktigt att följa instruktioner för underhåll och smörjning för att undvika för tidiga lagerskador.</p><p>Lindningen är klart den mest kritiska komponenten i våra analyser, detta är för att det är så mycket som spelar in lindningens funktion, koppartrådar, isoleringar, impregnering samt under själva processen kan det uppstå felsätt som påverkar lindningen negativt både direkt men även i ett senare skede då motorn är i drift.</p><p>Tätning av uttagslåda och lock beskriver de felsätt som kan uppstå då tätningen inte är effektiv och tät, vad som kan hända då det kommer in smuts och fukt och hur det eventuellt kan påverka plint eller andra elektriska applikationer. Felanalysen på fötter och sköldar behandlar mycket designfel, toleransfel och hur det påverkar komponenterna.</p><p>I rapporten beskrivs utförligt komponenternas funktioner, processer, underhåll samt det är i princip vad vi har kommit fram till i våra felanalyser fast vi i rapporten beskriver det mer utförligt. Efter beskrivning av varje komponent följer ett FMEA för respektive komponent och de FMEA:n är våra resultat av arbetet, där visas åtskilliga felsätt som är möjliga att uppstå hos varje komponent och även vilka felsätt som är mest kritiska att ta itu med.</p> / <p>In this report we discuss the tool, FMEA (Failure Mode Effect Analysis), we look at its usage and realization on identified components that are included in electrical motors made at ABB LV Motors in Västerås. Our goals with this work have been to give knowledge about the method FMEA to the company so they can learn to use this tool and for us to implement the tool on an existing application to analyze the failures that can occur. Since ABB have a project ongoing with a new motor our analyses can also give them input on what changes and actions to take on the new motor.</p><p>After two weeks training in the workshop and a thorough survey of the companys warranty claims we could define our work by identifying which components we would survey our analyses on. After a run through with our instructors at the company we draw the conclusion to survey our work on five components, which there are two components that are very critical that we discovered during our information collection with the warranty claims.</p><p>The analyzed components are:</p><p>1. Bearing</p><p>2. Winding</p><p>3. Sealing of terminal box and terminal cap.</p><p>4. Motor feet’s</p><p>5. Shields</p><p>Bearing which is after the winding the most critical component, it is a sensitive part and it is very important to follow instructions for maintenance and lubrication to avoid early bearing damages.</p><p>Winding is the clearest most critical component in our analyzes, this is because there is many parts that plays role in the windings function, copper wires, isolations, impregnation and during the process you can have failures that affect the winding in a negative way immediately but also in a later phase when the motor is running.</p><p>Sealing of the terminal box and cap describes those failures that can occur when the sealing is not effective and compact, and what happens is that you get dirt and damp in to the box and how it possibly can affect the connection box and other electrical applications.</p><p>The failure analyze on feets and shields deals with design faults, tolerance faults and how it affects the components.</p><p>In the report we describe the functions of the components, processes, maintenance and that is in general what we have in our FMEA analyses, however in the report we describe it more thoroughly. After the description of every component we have the FMEA of respective component attached. The FMEA analyses are our results in this work, in the analyses we show many possible failures at each component and also which failures are most critical to deal with.</p>

I denna rapport behandlas verktyget FMEA (Failure Mode Effect Analysis), dess användning och genomförande på identifierade komponenter som ingår i elmotorer tillverkade hos ABB LV Motors i Västerås. Målet med detta arbete har varit att ge lärdom om metoden FMEA på företaget för att möjliggöra användandet av verktyget bland medlemmarna på företaget och att vi implementerar verktyget på befintlig motor för att analysera de felsätt som kan uppstå. Eftersom ABB just nu har ett pågående projekt med en ny motor kan våra slutgiltiga felanalyser även ge dem information om vilka ändringar och åtgärder som kan vara till nytta vid utformningen av den nya motorn. Efter två veckors praktik i verkstan och en grundlig genomgång av företagets reklamationer kunde vi avgränsa arbetet genom att identifiera de komponenter som analyserna skulle genomföras på. Efter en genomgång tillsammans med våra handledare på företaget kom vi fram till att utföra våra analyser på fem komponenter, två av dem framstod som mycket kritiska som upptäcktes redan vid genomgång av reklamationerna. Komponenterna som vi har analyserat är: 1. Lager 2. Lindning 3. Tätning av uttagslåda och uttagslock 4. Fötter 5. Sköldar Lager som är den näst största kritiska komponent som vi tittat på är en del i motorn som är väldigt känslig, det är viktigt att följa instruktioner för underhåll och smörjning för att undvika för tidiga lagerskador. Lindningen är klart den mest kritiska komponenten i våra analyser, detta är för att det är så mycket som spelar in lindningens funktion, koppartrådar, isoleringar, impregnering samt under själva processen kan det uppstå felsätt som påverkar lindningen negativt både direkt men även i ett senare skede då motorn är i drift. Tätning av uttagslåda och lock beskriver de felsätt som kan uppstå då tätningen inte är effektiv och tät, vad som kan hända då det kommer in smuts och fukt och hur det eventuellt kan påverka plint eller andra elektriska applikationer. Felanalysen på fötter och sköldar behandlar mycket designfel, toleransfel och hur det påverkar komponenterna. I rapporten beskrivs utförligt komponenternas funktioner, processer, underhåll samt det är i princip vad vi har kommit fram till i våra felanalyser fast vi i rapporten beskriver det mer utförligt. Efter beskrivning av varje komponent följer ett FMEA för respektive komponent och de FMEA:n är våra resultat av arbetet, där visas åtskilliga felsätt som är möjliga att uppstå hos varje komponent och även vilka felsätt som är mest kritiska att ta itu med. / In this report we discuss the tool, FMEA (Failure Mode Effect Analysis), we look at its usage and realization on identified components that are included in electrical motors made at ABB LV Motors in Västerås. Our goals with this work have been to give knowledge about the method FMEA to the company so they can learn to use this tool and for us to implement the tool on an existing application to analyze the failures that can occur. Since ABB have a project ongoing with a new motor our analyses can also give them input on what changes and actions to take on the new motor. After two weeks training in the workshop and a thorough survey of the companys warranty claims we could define our work by identifying which components we would survey our analyses on. After a run through with our instructors at the company we draw the conclusion to survey our work on five components, which there are two components that are very critical that we discovered during our information collection with the warranty claims. The analyzed components are: 1. Bearing 2. Winding 3. Sealing of terminal box and terminal cap. 4. Motor feet’s 5. Shields Bearing which is after the winding the most critical component, it is a sensitive part and it is very important to follow instructions for maintenance and lubrication to avoid early bearing damages. Winding is the clearest most critical component in our analyzes, this is because there is many parts that plays role in the windings function, copper wires, isolations, impregnation and during the process you can have failures that affect the winding in a negative way immediately but also in a later phase when the motor is running. Sealing of the terminal box and cap describes those failures that can occur when the sealing is not effective and compact, and what happens is that you get dirt and damp in to the box and how it possibly can affect the connection box and other electrical applications. The failure analyze on feets and shields deals with design faults, tolerance faults and how it affects the components. In the report we describe the functions of the components, processes, maintenance and that is in general what we have in our FMEA analyses, however in the report we describe it more thoroughly. After the description of every component we have the FMEA of respective component attached. The FMEA analyses are our results in this work, in the analyses we show many possible failures at each component and also which failures are most critical to deal with.

When an exam is administered across dual platforms, such as paper-and-pencil and computer-based testing simultaneously, individual items may become more or less difficult in the computer version (CBT) as compared to the paper-and-pencil (P&P) version, possibly resulting in a shift in the overall difficulty of the test (Mazzeo & Harvey, 1988). Using 38,955 examinees' response data across five forms of the National Nurse Aide Assessment Program (NNAAP) administered in both the CBT and P&P mode, three methods of differential item functioning (DIF) detection were used to detect item DIF across platforms. The three methods were Mantel-Haenszel (MH), Logistic Regression (LR), and the 1-Parameter Logistic Model (1-PL). These methods were compared to determine if they detect DIF equally in all items on the NNAAP forms. Data were reported by agreement of methods, that is, an item flagged by multiple DIF methods. A kappa statistic was calculated to provide an index of agreement bet ween paired methods of the LR, MH, and the 1-PL based on the inferential tests. Finally, in order to determine what, if any, impact these DIF items may have on the test as a whole, the test characteristic curves for each test form and examinee group were displayed. Results indicated that items behaved differently and the examinee's odds of answering an item correctly were influenced by the test mode administration for several items ranging from 23% of the items on Forms W and Z (MH) to 38% of the items on Form X (1-PL) with an average of 29%. The test characteristic curves for each test form were examined by examinee group and it was concluded that the impact of the DIF items on the test was not consequential. Each of the three methods detected items exhibiting DIF in each test form (ranging from 14 items to 23 items). The Kappa statistic demonstrated a strong degree of agreement between paired methods of analysis for each test form and each DIF method pairing (reporting good to excell ent agreement in all pairings). Findings indicated that while items did exhibit DIF, there was no substantial impact at the test level.

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The credibility of unsupervised exams, one of the biggest challenges of e-learning, is currently maintained by proctoring. However, little has been done to determine whether expensive and inconvenient proctoring is necessary. The purpose of this quantitative study was to determine whether the use of security mechanisms, based on the taxonomy of cheating reduction techniques rooted in the fraud triangle theory, can be an effective alternative to proctoring. A quasi-experimental 1 group sequential design was used to answer the research questions whether the format, proctored versus unproctored, order in which the exams are administered, course delivery mode, and instructor make a difference in student performance. The archival scores of 850 Californian community college students on 2 sets of equivalent proctored and unproctored web-based exams in face-to-face, hybrid, and online introductory statistics courses taught by 7 instructors were compared. The format effect was tested with repeated-measures ANOVA; the order, course delivery mode and instructor effects were tested with mixed ANOVA. No significant difference in scores in Set 1, and significantly lower scores on unproctored exams in Set 2 indicated that the used security mechanisms allowed for maintaining the credibility of the exams without proctoring. There was no significant difference in scores across the course delivery modes in both sets and instructors in Set 2, but significant order effect was observed. Further research on order effect was recommended. With the use of the utilized security mechanisms education will get an inexpensive and convenient way to increase the credibility of unsupervised web-based exams, and the society will gain more online college graduates with credentials that reflect their knowledge.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Usage of mixed mode is popular among researchers mainly because of its flexibility and possibility of lowered costs, time and response bias. Therefore engagement in analysis of potential differences and problems is necessary mainly in mapping effects of these combinations. My diploma thesis focuses on possibilities of equivalence of mixed mode PAPI and CAPI collection of data. I use Multi-Group Confirmatory Factor Analysis (MG CFA) for analysing the equivalence of measurement. This is quite unusual method for analysing a mixed-mode and few researchers used it till now. Most of the studies focused on differences in answers of respondents caused by mode effect or cognitive process of answering the questions. Key words: Mixed mode design, equivalence, confirmatory factor analysis, CAPI and PAPI, mode effect