Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and LSTM Autoencoder.

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states-two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model.

Preventing Forklift Front-End Failures: Predicting the Weight Centers of Heavy Objects, Remaining Useful Life Prediction under Abnormal Conditions, and Failure Diagnosis Based on Alarm Rules.

This paper addresses the critical challenge of preventing front-end failures in forklifts by addressing the center of gravity, accurate prediction of the remaining useful life (RUL), and efficient fault diagnosis through alarm rules. The study's significance lies in offering a comprehensive approach to enhancing forklift operational reliability. To achieve this goal, acceleration signals from the forklift's front-end were collected and processed. Time-domain statistical features were extracted from one-second windows, subsequently refined through an exponentially weighted moving average to mitigate noise. Data augmentation techniques, including AWGN and LSTM autoencoders, were employed. Based on the augmented data, random forest and lightGBM models were used to develop classification models for the weight centers of heavy objects carried by a forklift. Additionally, contextual diagnosis was performed by applying exponentially weighted moving averages to the classification probabilities of the machine learning models. The results indicated that the random forest achieved an accuracy of 0.9563, while lightGBM achieved an accuracy of 0.9566. The acceleration data were collected through experiments to predict forklift failure and RUL, particularly due to repeated forklift use when the centers of heavy objects carried by the forklift were skewed to the right. Time-domain statistical features of the acceleration signals were extracted and used as variables by applying a 20 s window. Subsequently, logistic regression and random forest models were employed to classify the failure stages of the forklifts. The F1 scores (macro) obtained were 0.9790 and 0.9220 for logistic regression and random forest, respectively. Moreover, random forest probabilities for each stage were combined and averaged to generate a degradation curve and determine the failure threshold. The coefficient of the exponential function was calculated using the least squares method on the degradation curve, and an RUL prediction model was developed to predict the failure point. Furthermore, the SHAP algorithm was utilized to identify significant features for classifying the stages. Fault diagnosis using alarm rules was conducted by establishing a threshold derived from the significant features within the normal stage.

Computed tomography-guided percutaneous hook wire localization of pulmonary nodular lesions before video-assisted thoracoscopic surgery: Highlighting technical aspects.

OBJECTIVE: Confirming the histologic diagnosis of small pulmonary nodules or Ground-glass opacity nodules (GGNs) of unknown origin is difficult. These nodules are not always appropriate for percutaneous transthoracic needle biopsy. Preoperative localization of pulmonary lesions provides more precise target points to ensure complete surgical excision. The goal of the present study was to evaluate the validity and effectiveness of computed tomography-guided preoperative hook wire localization with our technique for video-assisted thoracoscopic surgery (VATS). METHODS: We retrospectively investigated 113 patients who had undergone preoperative hook wire localization before VATS resection for newly present or growing pulmonary nodular lesions between May 2007 and December 2016. Procedural and perioperative outcomes were assessed to evaluate the safety and efficacy of preoperative localization technique. RESULTS: A total of 113 pulmonary nodules were localized and successfully resected in all 113 patients. The mean diameter of nodules was 10.8 ± 6.1 mm (range, 3-28). The mean distance from the pleural surface was 20.2 ± 12.4 mm (range, 5-55). The mean procedure time of localization was 23.7 ± 6.3 min. Asymptomatic minimal pneumothorax and mild parenchymal hemorrhage occurred in 26 (23.0%) and 8 (7.1%) patients, respectively. There were 32 (28.3%) deep lung nodules, in which the distance to pleural surface was more than 25 mm. Wire dislodgement occurred in 4 (3.5%) patients. Complete resection of all lung lesions was achieved, and definite histological diagnosis was obtained in all patients. Pathologic examination revealed 42 (37.2%) primary lung cancers, 2 (1.8%) lymphomas, 53 (46.9%) metastases, 16 (14.1%) benign lesions. CONCLUSIONS: Preoperative percutaneous hook wire localization is a dependable and useful technique to facilitate positioning small and deep pulmonary nodules for thoracoscopic complete excision and accurate diagnosis.

Increased expression of MIP-3alpha/CCL20 in peripheral blood mononuclear cells from patients with ulcerative colitis and its down-regulation by sulfasalazine and glucocorticoid treatment.

UNLABELLED: CCL20 expression is known to increase in the mucosal tissues of inflammatory bowel diseases (IBDs). Moreover, the discovery of Nod2 as the IBD1 susceptibility gene has underscored the significance of blood mononuclear cells in IBD pathogenesis. METHODS: This study addresses whether CCL20 expression is similarly altered in peripheral blood mononuclear cells (PBMCs) of patients with ulcerative colitis (UC), a major type of IBD in Korea. RESULTS: Expression of CCL20 was significantly up-regulated in the PBMCs of patients with UC compared with those of normal healthy controls. Interestingly, untreated UC groups expressed higher levels of CCL20 mRNA than either treated UC or normal control groups, suggesting that CCL20 could be modulated by anti-inflammatory drugs. Accordingly, a strong association between CCL20 levels and disease activity index was observed. Supporting these findings, results from a 3-month follow-up study revealed that the UC groups treated with 5-aminosalicylic acid and glucocorticoid exhibited dramatic decreases of CCL20 mRNA in PBMCs, accompanied by ameliorated disease states. Moreover, tumor necrosis factor-alpha- or interleukin-1beta-induced CCL20 secretion was greatly diminished by 5-aminosalicylic acid and/or glucocorticoid treatment of human intestinal epithelial HT-29 cells. Of note, CCR6 cell populations were significantly reduced in the blood of severe patients with UC compared with normal controls, whereas no significant changes in CCR6 cell populations were observed in the blood of patients with mild UC or acute colitis. CONCLUSIONS: Collectively, these findings suggest that CCL20 expression in blood mononuclear cells is associated with altered immune and inflammatory responses in patients with UC.