The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient communication between the sensors, gateway devices, and the cloud server. The system was tested on an operational motors dataset, five machine learning algorithms, namely k-nearest neighbor (KNN), supported vector machine (SVM), random forest (RF), linear regression (LR), and naive bayes (NB), are used to analyze and process the collected data to predict motor failures and offer maintenance recommendations. Results demonstrate the random forest model achieves the highest accuracy in failure prediction. The solution minimizes downtime and costs through optimized maintenance schedules and decisions. It represents an Industry 4.0 approach to sustainable smart manufacturing.
The fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature‐based datasets often perform poorly in ZD detection. A new technique for detecting zero‐day (ZD) attacks in IoT‐based Conventional Spiking Neural Networks (CSNN), termed ZD‐CSNN, is proposed. The model comprises three key levels: (1) Data Pre‐processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and t
In this paper, the reliability and scheduling of maintenance of some medical devices were estimated by one variable, the time variable (failure times) on the assumption that the time variable for all devices has the same distribution as (Weibull distribution.
The method of estimating the distribution parameters for each device was the OLS method.
The main objective of this research is to determine the optimal time for preventive maintenance of medical devices. Two methods were adopted to estimate the optimal time of preventive maintenance. The first method depends on the maintenance schedule by relying on information on the cost of maintenance and the cost of stopping work and acc
... Show MoreObjective(s): To determine the effectiveness of lactation counseling on maintenance of breastfeeding after
cesarean section for study group.
Methodology: A quasi-experimental study was conducted on non probability sample (purposive) of (60) women
who had cesarean section and sample divided in to two groups (30) of them as a study group from Fatima AlZahra'a
Maternity and Pediatric Teaching Hospital & implement lactation counseling on it and another group (30)
as control group from Baghdad Teaching Hospital in Baghdad City. Study implemented for the period of April 24th
2011 to August 2nd 2011. A questionnaire was used as a tool of data collection to fulfill with objective of the study
and consisted of three parts
A lack of adequate building maintenance is a significant obstacle faced by governmental hospitals. This paper evaluates factors that negatively impact building-maintenance practices in Iraq. A literature review was conducted to identify factors affecting maintenance. A list of 42 factors affecting hospital-buildings was collected from previous studies and tested using a structured questionnaire distributed to hospital-maintenance experts. During the data analysis, 76 valid questionnaires were used. Based on the respondents’ ratings, the relative-importance index (RII) was used to determine the level of importance of each factor. From the results, it was concluded that twelve factors affect maintenance practices in hospital buildin
... Show MoreImpact of buildings in Iraq and other countries on the environment is obvious; this problem began to take scientific and humanitarian dimensions in order to reduce and eliminate this problem. This impact can be seen through the energy, water and raw materials consumed for the establishment, operation and maintenance of these buildings, as well as the emissions of hazardous gases and generations of solid wastes.
This work was conducted to assess the current maintenance managerial practice for the governmental buildings to stand on the main obstacle and extrapolation of measures by means of interviews with experts to determine the effective factors and closed questionnaire to state the features and the need for new
... Show MoreA three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
... Show MoreWith the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect
... Show More