Improved canonical correlation analysis for detecting faults related to key performance indicator

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer’s Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1–10 and ∼0.1–0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.

As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

Standard partial least square (PLS) is a useful tool for process monitoring; however, it still encounters some problems for the diagnosis of key performance indicator (KPI) faults. One of its recent modifications, improved PLS (IPLS), decomposes the process measurements into KPI‐related and KPI‐unrelated parts according to the correlation matrix obtained from the standard PLS. The entire residual space of PLS is categorized as the IPLS's KPI‐unrelated part. However, the residual space still involves some information related to KPI, and hence IPLS's decomposition may be inappropriate. In this study, a new modified PLS is proposed, which also decomposes the residual space according to the KPI. The loadings of input data are first decomposed to obtain a projection model. Next, the input data are more appropriately decomposed into KPI‐related and KPI‐unrelated parts. Correspondingly, two statistic indices can be designed for fault diagnosis. A numerical example and the Tennessee‐Eastman (TE) benchmark process are utilized to demonstrate the effectiveness and advantages of the proposed approach.

Multimode processes may be operating under different statuses, revealing different process characteristics and variations. It can be an interesting issue to check how the process variations change from one mode to another and thus how they can be used for multimode analysis and monitoring. In the present work, a between-mode relative analysis algorithm is proposed to explore the between-mode relationship. Comprehensive subspace decomposition is performed in each mode regarding their relative changes from the reference mode and influences on process monitoring. Each mode is separated into three different systematic subspaces and one residual subspace based on the between-mode changes of process variations, including the increased part, the decreased part, and the unchanged part. These different variations are then modeled respectively and different monitoring statistics are defined for online monitoring to identify mode affiliation and detect the fault status. By separating and monitoring different types of between-mode relative variations, the proposed method can not only efficiently distinguish different modes and detect faults but also offer enhanced process understanding. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with multimode data generated from the Tennessee Eastman (TE) benchmark process.

Switching LDS-based approach for process fault detection and classification

Partial least squares (PLS) is an efficient tool widely used in multivariate statistical process monitoring. Since standard PLS performs oblique projection to input space X, it has limitations in distinguishing quality-related and quality-unrelated faults. Several postprocessing modifications of PLS, such as total projection to latent structures (T-PLS), have been proposed to solve this issue. Further studies have found that these modifications fail to reduce false alarm rates (FARs) of quality-unrelated faults when fault amplitude increases. To cope with this problem, this paper proposes an enhanced quality-related fault detection approach based on orthogonal signal correction (OSC) and modified-PLS (M-PLS). The proposed approach removes variation orthogonal to output space Y from input space X before PLS modeling, and further decomposes X into two orthogonal subspaces in which quality-related and quality-unrelated statistical indicators are designed separately. Compared with T-PLS, the proposed approach has a more robust performance and a lower computational load. Two case studies, including a numerical example and the Tennessee Eastman (TE) process, show the effeteness of the proposed approach.

This research is to analyze the process of development of quality management by using the cycled approach of PDCA (Plan-Do-Check-Act) based on KPI (Key Performance Indicators). This research was conducted at SMP-SMA Integral Ar-Rohmah "Boarding School" Dau Malang by using descriptive qualitative methods. The object of this research is around 449 students at SMP-SMA Integral Ar-Rohmah Dau Malang. The research period is the even semester of the 2018/2019 school year. The results of this study can be concluded that; Planning (Plan), produces three KPI formulation products, namely; a) KPI monitoring; b) KPI filling formula; and c) KPI report card. Implementation (Do), by requiring all students to participate in filling the KPI monitoring form and activeness of the musyrif room in assisting the filling of KPI. Evaluation (Check) that has been carried out is by means of a random check (random) conducted by the room meeting from the results of filling KPI monitoring. The follow up (Act) is to publish KPI report cards.

In the majority of the multivariable processes, analysis of process monitoring and fault diagnosis is usually based on the fundamental assumption that the monitored variables follow a Gaussian distribution. However, it is well known that many of the variables are mutually dependent in process systems. This paper proposes a new monitoring method based on independent component analysis (ICA) - sparse autoencoder. The independent information component can be extracted by ICA through higher-order statistics. Moreover, the inherent nonlinear characteristics in the residual model of ICA can be handled by a deep architecture constructed with sparse autoencoder. To overcome the problem of local minima in the optimization of sparse autoencoder, a restricted Boltzmann machine (RBM) is used to pre-train the net, and the parameters in the sparse autoencoder is updated by Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Monitoring statistic is developed and its confidence limit is computed by kernel density estimation. A case study of the Tennessee Eastman (TE) benchmark process indicates that the proposed fault detection method is more efficient.

Common partial least squares (PLS) is used to a power way in the multivariate statistical process monitoring filed for the past two decades. However, PLS takes an incomplete decomposition that fails to work well in the quality-related fault detection and diagnosis. To address this situation, In this paper, to solve these problems, total principal component analysis (TPCR) is analyzed in detail which can separate the process variables into two specific portions, the quality-related portion and quality-unrelated portion. Statistics of TPCR are designed to offer the detection results regarding abnormal conditions. To figure out the fault-related variables, the calculation of the contribution of variables is given based on the contribution plots. A corresponding control limit is determined to recognize fault corresponding variables. Furthermore, the fault diagnosis logic is proposed in this paper. Finally, the Tennessee Eastman (TE) model is taken as an example to prove the performance of TPCR in quality-related process monitoring.

Human age, gender and ethnicity are valuable demographic information about a population. These measures are also considered important soft biometric traits for human recognition or search. Usually the three traits are studied separately. A recent study [9] shows that the three traits can be estimated simultaneously based on a multi-label regression formulation. The linear and kernel partial least squares (PLS) models are adopted to solve the multi-label regression problem in [9]. In this study, we investigate the canonical correlation analysis (CCA) based methods, including linear CCA, regularized CCA (rCCA), and kernel CCA (KCCA), and compare to the PLS models in solving the joint estimation problem. Interestingly, we found a consistent ranking of the five methods in estimating age, gender, and ethnicity. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity, which has not been shown in previous research, to the best of our knowledge. The experiments are conducted on a very large database of more than 55,000 face images.

Usually, industrial processes have multiple operational modes due to different production strategies, external environmental variability, or changes in product specifications. Monitoring of multimode processes constitutes a challenging problem considering multiple steady-state operational regions and dynamic transitions. This paper proposes a novel method for the offline identification of stable modes and transitions based on a local kernel density estimation algorithm. The online monitoring scheme is based on mode identification and transition tracking. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposal. As a result, stable modes are successfully isolated from transitions, even when these involve complex changes in the production mode. The results also demonstrate that the proposed scheme is capable of tracking mode changes, and finally, results monitored during transitions confirm the validity and efficacy of the new approach compared with pr...

Sparse partial least-squares regression and its applications to high-throughput data analysis

This paper aims to describe the development of a WebGIS application for the Muhammadiyah Regional Board of Central Java as a monitoring tool for the organization's Key Performance Indicator. The initial phase of the project involved conducting Focus Group Discussions (FGDs) to collect data regarding the organization's needs and requirements. Furthermore, the design process involved creating a comprehensive inventory of application functions and user interfaces. The use of WebGIS technology enables monitoring of key performance indicators (KPIs) and integration of spatial data. This enables more comprehensive visualization of performance data through interactive dashboards and spatial visualization. Further enhancements to the application were designed based on a thorough stakeholder review, taking into account comments obtained through the stakeholder review process. Once the application reached a satisfactory level of performance, it was formally introduced to stakeholders through a socialization process. The application was started and its security was verified using the reliable hypertext transfer protocol (HTTPS) via the URL https://kpi.pwmjateng.id. The implementation of the KPI monitoring tool based on WebGIS technology has yielded good results, and it is showing its success within the Muhammadiyah Regional Board of Central Java. Now, they can effectively monitor and track their indicators.

Comparative study of different structural descriptors and variable selection approaches using partial least squares in quantitative structure-activity relationships

Joint-individual monitoring of large-scale chemical processes with multiple interconnected operation units incorporating multiset CCA