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This is part 1 for week 8. I will submit a separate for part 2 week 8.Submit your completed project by Day 7 of this week. For detailed instructions on completing this assignment, see the associated...

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PRÜFUNGSAMT
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PORTFOLIO
Assignments for the course: Project: Software Engineering
(DLMCSPSE01)
TABLE OF CONTENTS
1. TOPICS AND TASKS ................................................................................................... XXXXXXXXXX2
1.1. Task 1: Design and development of a web application .................................................... XXXXXXXXXX2
1.1.1. Conception phase ................................................................................................................ XXXXXXXXXX3
1.1.2. Development phase
eflection phase ................................................................................ XXXXXXXXXX3
1.1.3. Finalization phase ............................................................................................................... XXXXXXXXXX4
1.2. Task 2: Design and development of a mobile application................................................. XXXXXXXXXX5
1.2.1. Conception phase ................................................................................................................ XXXXXXXXXX6
1.2.2. Development phase
eflection phase ................................................................................ XXXXXXXXXX7
1.2.3. Finalization phase ............................................................................................................... XXXXXXXXXX7
2. TUTORIAL SUPPORT ................................................................................................. XXXXXXXXXX8
3. EVALUATION ............................................................................................................ XXXXXXXXXX9
4. FORMAL GUIDELINES AND SPECIFICATIONS FOR SUBMISSION .................................... XXXXXXXXXX10
4.1. Components of the examination performance .................................................................... XXXXXXXXXX10
4.2. Format for Digital File Submission ........................................................................................ XXXXXXXXXX11
4.3. Format of Abstract ................................................................................................................. XXXXXXXXXX13
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PRÜFUNGSAMT
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1. TOPICS AND TASKS
Within the framework of this course, one of the following topics must be selected.
1.1. Task 1: Design and development of a web application
Digital transformation is changing the way how we work and live, how we do business. Software enables new
usiness models, improves business processes, introduces digitized products and services. Nowadays, ready-to-
use frameworks and technologies allow software engineers to quickly develop and deploy new, innovative appli-
cations. In this project, you will design and develop such a web application.
It is up to you and your creativity to determine what type of application you will develop. However, your project
and the upcoming application must satisfy the following criteria:
• Your application must deliver customer value, i.e., it must have a clear and significant benefit to a de-
fined target group.
• Your upcoming solution must be something new. You are not allowed to simply copy an existing appli-
cation.
• You must implement a reasonable amount of code. A mock-up with slides or a simple configuration of an
existing software is not sufficient as final product. Such a step can be helpful in the conception phase to
explore and discuss alternatives.
• The application must run in a web
owser.
In this course, you will work through all stages of a software development project. It is important that you
select the appropriate methodologies, techniques, and tools at every stage and for every task. This means that
you will apply the knowledge in the fields of requirements specification, software design, implementation, and
testing that you have acquired in prior modules.
As a considerable percentage of software engineering projects fails to deliver in time, quality, and budget, you
should take appropriate actions to minimize risks in every phase.
The following acceptance criteria must be met:
• The tools, programming language and li
aries used to implement your application are your own choice.
However, you are not permitted to copy or modify existing, third-party applications that you might find
on the internet or elsewhere. You can use third-party li
aries if you indicate them co
ectly.
• The application must come with a web-based graphical user interface that is self-explanatory and that
can be intuitively used by potential customers.
• The solution delivered must be valuable to your customers. It must provide the most important features
and solve the most basic problems of your target-group. You must provide enough and sensible (test)
data (no dummy text) so that the application can directly be used, and its benefits become tangible.
• The application must meet the non-functional requirements. If this is not possible in the scope of the
project, it must be described as technical debt.
• The software must come up with an appropriate project documentation, including a project profile, re-
quirements documents, a documentation of the software and system architecture.
• Version and release control of the software and the co
esponding architecture documentation must be
established.
• The source code and all necessary files must be provided in GitHub. Hosting the project on GitHub is part
of building your portfolio.
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• Web applications must be provided as cloud-hosted application (e.g. AWS, Google Cloud, …) so that the
front end can directly be accessed.
• The software must come with installation and run instructions.
Your application needs to be designed, built, documented, and delivered according to the following three
phases.
1.1.1. Conception phase
This phase is very important for the success of your project. Anything that is overlooked or
Answered 2 days After Nov 01, 2024

Solution

Bhaumik answered on Nov 04 2024
3 Votes
Predictive Maintenance Using Machine Learning: A Detailed Methodology
I. INTRODUCTION
The goal of predictive maintenance is to estimate the likelihood of an industrial equipment failure so that preventive maintenance can minimize unplanned downtime. To forecast equipment failure, this project involves developing a machine-learning model utilizing past sensor data. Preventing malfunctions and optimizing maintenance schedules are the objectives.
II. DATASET OVERVIEW
Industrial equipment failure logs and sensor measurements constitute the dataset used in this study. Air temperature, process temperature, torque, rotational speed, and tool wear are important characteristics. The binary column Target, which represents the target variable, indicates whether a failure occu
ed (1) or not (0).
Columns:
· UDI: Unique Identifier.
· Product ID: ID of the product.
· Type: Categorical variable indicating product type.
· Air Temperature [K]: Air temperature in Kelvin.
· Process Temperature [K]: Process temperature in Kelvin.
· Rotational Speed [rpm]: Speed in revolutions per minute.
· Torque [Nm]: Torque in Newton-meters.
· Tool wear [min]: Tool wear in minutes.
· Target: Binary Failure target (0=No failure, 1=Failure).
Initial Data Inspection: The dataset is made up of 10 columns, no missing values were discovered at first. However, further data modifications produced missing values that were properly handled.
III. METHODOLOGY
3.1 Data Preprocessing:
Preparing the data was the initial stage in creating the predictive maintenance model. The dataset given comprised of several sensor readings and product information, including category factors, numerical variables, and the goal failure labels. Initial exploratory data analysis (EDA) indicated that several columns, such as UDI and Product ID, were identifiers rather than features that may add to the model’s predictive performance. As a result, these columns were removed since they included no information that might be used to anticipate equipment failure. Similarly, as the main objective of the classification model was to predict whether a failure happened rather than identifying the precise type of failure, the column Failure Type, which described the type of failure for failed units, was not explicitly employed as a feature in the model.
3.1.1 Dropping Unnecessary Columns:
UDI, Product ID, and Failure Type are examples of columns that were removed because they are either identifiers or not predictive for the cu
ent job.
data_cleaned = data.drop(['UDI', 'Product ID', 'Failure Type'], axis=1)
3.1.2 Handling Categorical Values:
A one-hot encoding process was used to transform the categorical Type column into a numerical format for model training. By assigning random numbers, one-hot encoding...
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