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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume></Volume>
				<Issue>Articles in Press</Issue>
				<PubDate PubStatus="epublish">
					<Year>2023</Year>
					<Month>06</Month>
					<Day>20</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Constructing a Sensitive Control Chart to Monitor Process Mean using Optimal Filter: Time-Frequency Analysis Approach</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage></FirstPage>
			<LastPage></LastPage>
			<ELocationID EIdType="pii">23274</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2023.61463.7321</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Maryam</FirstName>
					<LastName>Askari</LastName>
<Affiliation>Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Orod</FirstName>
					<LastName>Ahmadi</LastName>
<Affiliation>Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran</Affiliation>
<Identifier Source="ORCID">0000-0002-8879-5164</Identifier>

</Author>
<Author>
					<FirstName>Youness</FirstName>
					<LastName>Javid</LastName>
<Affiliation>Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2022</Year>
					<Month>11</Month>
					<Day>19</Day>
				</PubDate>
			</History>
		    <Abstract>Control charts are one of the critical tools for process monitoring. Test statistics are computed as a function of sample means in control charts for monitoring the process mean. In this study, these functions are modeled by filters. These filters have essential properties in both time and frequency domains. In previous studies, only their properties in the time domain have been considered. Thus, the resulting filters have sub-optimal performance. This study investigates the optimal design of these filters for monitoring the process mean. The behavior of these filters is analyzed not only in the time domain but also in the frequency domain. Properties such as stability, causality, minimum phase, and lowpass are considered in designing this filter. An optimization model is designed and solved using a Genetic Algorithm based on these properties to minimize the Average Run Length. The proposed optimal filter is compared with other control charts using simulation studies. Results showed the high speed of the proposed filter in detecting shifts in the process mean. The proposed optimal filter is also used to monitor the oil price of the OPEC basket. The results showed that shifts were detected at the right time using the proposed optimal filter.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Process monitoring</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Control chart</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">signal processing</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Filter Design</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Second-order-filter</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_23274_0d7194e1c7a4b32303ee3831a30c4ab0.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
