<p>One way to reduce the lifecycle cost and environmental
impact of a product in a circular economy is to extend its lifespan by either
creating longer-lasting products or managing the <a>product
properly during its use stage. Life extension of a product is envisioned to
help better utilize </a>raw materials efficiently and slow the rate of resource
depletion. In the case of manufacturing equipment (e.g., an electric motor on a
machine tool), securing reliable service life as well as the life extension are
important for consistent production and operational excellence in a factory. However,
manufacturing equipment is often utilized without a planned maintenance
approach. Such a strategy frequently results in unplanned downtime, owing to
unexpected failures. Scheduled maintenance replaces components frequently to
avoid unexpected equipment stoppages, but increases the time associated with
machine non-operation and maintenance cost. </p><p><br></p>
<p>Recently, the emergence of Industry 4.0 and smart systems is
leading to increasing attention to predictive maintenance (PdM) strategies that
can decrease the cost of downtime and increase the availability (utilization
rate) of manufacturing equipment. PdM also has the potential to foster
sustainable practices in manufacturing by maximizing the useful lives of
components. In addition, advances in sensor technology (e.g., lower fabrication
cost) enable greater use of sensors in a factory, which in turn is producing
greater and more diverse sets of data. Widespread use of wireless sensor
networks (WSNs) and plug-and-play interfaces for the data collection on
product/equipment states are allowing predictive maintenance on a much greater
scale. Through advances in computing, big data analysis is faster/improved and has
allowed maintenance to transition from run-to-failure to statistical
inference-based or machine learning prediction methods.</p><p><br></p>
<p>Moreover, maintenance practice in a factory is evolving from
equipment “health management” to equipment “wellness” by establishing an
integrated and collaborative manufacturing system that responds in real-time to
changing conditions in a factory. The equipment wellness is an active process
of becoming aware of the health condition and of making choices that achieve
the full potential of the equipment. In order to enable this, a large amount of
machine condition data obtained from sensors needs to be analyzed to diagnose the
current health condition and predict future behavior (e.g., remaining useful
life). If a fault is detected during this diagnosis, a root cause of a fault
must be identified to extend equipment life and prevent problem reoccurrence.</p><p><br></p>
<p>However, it is challenging to build a model capturing a
relationship between multi-sensor signals and mechanical failures, considering
the dynamic manufacturing environment and the complex mechanical system in
equipment. Another key challenge is to obtain usable machine condition data to
validate a method.</p><p><br></p>
<p>A goal of the proposed work is to develop a systematic tool
for maintenance in manufacturing plants using emerging technologies (e.g., AI,
Smart Sensor, and IoT). The proposed method will facilitate decision-making
that supports equipment maintenance by rapidly detecting a worn component and
estimating remaining useful life. In order to diagnose and prognose a health condition
of equipment, several data-driven models that describe the relationships
between proxy measures (i.e., sensor signals) and machine health conditions are
developed and validated through the experiment for several different manufacturing-oriented
cases (e.g., cutting tool, gear, and bearing). To enhance the robustness and
the prediction capability of the data-driven models, signal processing is
conducted to preprocess the raw signals using domain knowledge. Through this
process, useful features from the large dataset are extracted and selected,
thus increasing computational efficiency in model training. To make a decision
using the processed signals, a customized deep learning architecture for each
case is designed to effectively and efficiently learn the relationship between
the processed signals and the model’s outputs (e.g., health indicators).
Ultimately, the method developed through this research helps to avoid
catastrophic mechanical failures, products with unacceptable quality, defective
products in the manufacturing process as well as to extend equipment service
life.</p><p><br></p>
<p>To summarize, in this dissertation, the assessment of
technical, environmental and economic performance of the AI-driven method for
the wellness of mechanical systems is conducted. The proposed methods are applied
to (1) quantify the level of tool wear in a machining process, (2) detect
different faults from a power transmission mini-motor testbed (CNN), (3) detect
a fault in a motor operated under various rotation speeds, and (4) to predict
the time to failure of rotating machinery. Also, the effectiveness of
maintenance in the use stage is examined from an environmental and economic
perspective using a power efficiency loss as a metric for decision making
between repair and replacement.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14480046 |
| Date | 25 April 2021 |
| Creators | Wo Jae Lee (10695907) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/AI-DRIVEN_PREDICTIVE_WELLNESS_OF_MECHANICAL_SYSTEMS_ASSESSMENT_OF_TECHNICAL_ENVIRONMENTAL_AND_ECONOMIC_PERFORMANCE/14480046 |
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