Iman Izadi

Bio: Iman Izadi is an academic researcher from Isfahan University of Technology. The author has contributed to research in topics: ALARM & Constant false alarm rate. The author has an hindex of 22, co-authored 69 publications receiving 1365 citations. Previous affiliations of Iman Izadi include University of Alberta & Honeywell.

Similarity Analysis of Industrial Alarm Flood Data

Abstract: Flooding of alarms is a very crucial problem in process industries. An alarm flood makes an operator ineffective of taking necessary actions, and often risking an emergency shutdown or a major upset. In this work, the flooding of alarms is discussed based on the standards presented in ISA 18.2. A new analysis method is proposed to investigate similar alarm floods from the historic alarm data and group them on the basis of the patterns of alarm occurrences. A case study on real industrial alarm data is also presented to demonstrate the utility of the proposed analysis.

Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm

Abstract: Alarm flooding is one of the main problems in alarm management. Alarm flood pattern analysis is helpful for root cause analysis of historical floods and for incoming flood prediction. This paper deals with a data driven method for alarm flood pattern matching. An alarm flood is represented by a time-stamped alarm sequence. A modified Smith–Waterman algorithm considering the time stamp information is proposed to calculate a similarity index of alarm floods. The effectiveness of the algorithm is validated by a case study on actual chemical process alarm data.

Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application.

Abstract: In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.

Runs and Scans With Applications

Abstract: of the conditional distribution speciŽ cations. Chapters 8 and 10 extend these methods from two to more dimensions. Chapter 9 investigates estimation in conditionally speciŽ ed models. Chapter 11 considers models speciŽ ed by conditioning on events speciŽ ed by one variable exceeding a value rather than equaling a value, and Chapter 12 considers models for extreme-value data. Chapter 13 extends conditional speciŽ cation to Bayesian analysis. Chapter 14 describes the related simultaneous-equation models, and Chapter 15 ties in some additional topics. An appendix describes methods of simulation from conditionally speciŽ ed models. Chapters 1–4, plus Chapters 9 and 13, comprise a lively overview of conditionally speciŽ ed models. The remainder of the text constitutes a detailed catalog of results speciŽ c to different conditional distributions. Although this catalog is certainly of value, the reader desiring a briefer and less detailed introduction to the subject might skip the remainder at Ž rst reading. This text is a revision of the book by Arnold, Costillo, and Sarabia (1992). The current version is of similar breadth, but with much more depth than the original. The text is clearly written and accessible with relatively few mathematical prerequisites. I found surprisingly few typographical errors; the authors are to be congratulated for this. In a few cases, regularity conditions for results are not given in full. Generally, this causes little confusion, although something does appear to be missing in the statement of Aczél’s key theorem (Theorem 1.3). Fortunately, most of the results in the sequel are derived from corollaries to this theorem, and the corollaries are stated more precisely. I noted few gaps in the material covered. The only area that I thought was insufŽ ciently represented was application to Markov chain Monte Carlo. Conditional speciŽ cation is particularly important in Gibbs sampling. I believe that many practitioners would beneŽ t from a discussion of the issues involved in these sampling schemes. Each chapter contains numerous exercises. These exercises appear to be at an appropriate level for a graduate course in statistics, and appear to provide appropriate reinforcement for the material in the preceding chapters.