This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and WPT. A decision tree is used to structure intelligent diagnosis rules in each step until the states are fully and automatically detected. The efficacy of this method was confirmed by applying it to an experimental low-speed rotation machine.

Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient use of road capacity. To fully reap these benefits in the initial phases of technology deployment, careful planning of platoons based on trucks’ itineraries and time schedules is required. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research.

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Planning of truck platoons: A literature review and directions for future research

In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.

Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

A truck platoon is a set of virtually linked trucks that travel in tandem with small intervehicle distances. Several studies have proved that traveling in platoons can significantly improve fuel economy due to the reduced aerodynamic drag. However, most literature only provides scattered pieces of information regarding fuel economy in truck platoons. Therefore, a literature survey is needed to understand what has been studied and what problems remain to be further addressed. This paper presents an overview of existing studies to illustrate the state of the art about fuel savings for truck platooning. Specifically, it summarized the methodologies, the contributing factors of fuel consumption, the coordination methods to improve the platooning rate, and the look-ahead control strategies to generate fuel-efficient speed profiles for each vehicle driving in a platoon over different road grades. After that, the autonomous truck platooning was introduced, and we raised and discussed a couple of outstanding questions to be addressed in future work.

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Fuel Economy in Truck Platooning: A Literature Overview and Directions for Future Research

Pattern Recognition is one of the very important and actively searched trait or branch of artificial intelligence. It is the science which tries to make machines as intelligent as human to recognize patterns and classify them into desired categories in a simple and reliable way. This review paper introduces the basic concepts of pattern recognition, the underlying system architecture and provides the understanding of various research models and related algorithms for classification and clustering.

Classification in Pattern Recognition: A Review

As fuel accounts for a significant proportion of the total operational cost of heavy duty vehicles (HDVs) and to alleviate the environmental impacts, companies are seeking for novel practical methods to reduce fuel consumption Platooning is an effective approach in which a string of HDVs driving close behind each other is formed This reduces the aerodynamic drag leading to a reduction in the overall resistive force on vehicles which can provide an amount of fuel-saving in each of the following vehicles These trivial reductions result in a considerable decrease in the total fuel consumption corresponding to all the vehicles In this paper, we propose a mathematical model for the fuel-efficient platooning problem with a deadline for each vehicle (truck) to reach its destination by then We create a graph partly based on the German road network considering only 20 important cities and solve 50 instances including 10 to 50 trucks The small samples are solved by the LINDO solver in real time but as the problem has a high computational complexity, a Genetic Algorithm is applied to obtain fast solutions of a good quality for larger instances The final results show a satisfactory fuel-saving in all of the cases

The Fuel-Efficient Platooning of Heavy Duty Vehicles by Mathematical Programming and Genetic Algorithm

Driving trucks in a queue behind each other and in close proximity, called platooning, has been recently under consideration as a novel and promising approach to reduce fuel consumption, which provides environmental and financial benefits This method works since driving in the slipstream of another vehicle reduces the aerodynamic drag, and as a result, less energy or fuel is consumed This paper addresses this problem with the realistic assumptions of existing time constraints for trucks to depart from the origin and arrive at their destination, and waiting as well as detour possibility As this problem is NP-hard even in its very simplified forms, a new meta-heuristic solution methodology inspired from ant colony optimisation is proposed to deal with it Some sample problems of small to large size are generated and solved with our solution approach The analysis of results shows the satisfactory performance of this meta-heuristic and its superiority over the exact and our previous approach with genetic algorithm In addition, we analyse how the final result is affected by changing the main inputs and configurations of the problem

Fuel-efficient truck platooning by a novel meta-heuristic inspired from ant colony optimisation

Fault detection and diagnosis has an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance techniques which is applicable in the fault diagnosis of rotating machinery faults. We considered the Support Vector Machine (SVM) method for classifying the condition of centrifugal pump into two types of faults through six features: flow, temperature, suction pressure, discharge pressure, velocity, and vibration. The SVM method is based on statistical learning theory (SLT) and powerful for the problem with small sampling, nonlinear and high dimension. (L.V. Ganyun et al 2005). The SVM classifying is implemented with 4 kernel functions and the results of them are compared. We use an Artificial Neural Network (ANN) as the second classifying method to have comparison among the performance of two methods. After applying the two methods to our data set we make the data set noisy and again we try our SVMs and ANN to compare their robustness in noisy conditions and the results obtained from two methods confirmed the superiority of SVM with some specific kernel functions.

Comparing performance and robustness of SVM and ANN for fault diagnosis in a centrifugal pump

Fault diagnosis and detection play a crucial role in every system for its safe operation and long life. Condition monitoring is an applicable and effective method of maintenance techniques in the fault diagnosis of rotating machinery. In this paper two outstanding heuristic classification approaches, namely Artificial Neural Network ANN and Support Vector Machine SVM with four different kernel functions are applied to classify the condition of a real centrifugal pump belonging to petroleum industry into five different faults through six features which are: flow, temperature, suction pressure, discharge pressure, velocity and vibration. To increase the power of our classifiers, they are trained and tuned by Genetic Algorithm GA which is an effective evolutionary optimisation method. The experiments are done once with normal data and another time with noisy data in order to examine how robust the approaches are. Finally, the classification results of ANN-GA, SVM-GA, pure ANN and SVM without GA enhancements along with other two practical classification algorithms, namely K-Nearest Neighbours KNN and Decisions Tree, are compared together in terms of different aspects.

Fault Classification of a Centrifugal Pump in Normal and Noisy Environment with Artificial Neural Network and Support Vector Machine Enhanced by a Genetic Algorithm

In this paper, the problem of driving vehicles behind each other in close proximity as a file to reduce the total fuel consumption, called fuel-efficient platooning (FEP), is investigated. Some real-life attitudes like time restrictions and multiple allowable speeds for vehicles are taken into account. Linear mathematical formulations are presented for the FEP problem, which are coded in GAMS and solved by the GUROBI solver. Since the problem has a high computational complexity, an alternative evolutionary solution approach with Particle Swarm Optimisation (PSO) is proposed. An appropriate application procedure is given, which converts the continuous solution space of PSO into the routing, time scheduling and speed adjustment. The performance of our PSO is tested with some generated samples including up to 1000 vehicles on the graph of Chicago road network. The results verify the goodness of our PSO in terms of solution quality and time.

Abtin Nourmohammadzadeh

Papers

The Fuel-Efficient Platooning of Heavy Duty Vehicles by Mathematical Programming and Genetic Algorithm

Fuel-efficient truck platooning by a novel meta-heuristic inspired from ant colony optimisation

Comparing performance and robustness of SVM and ANN for fault diagnosis in a centrifugal pump

Fault Classification of a Centrifugal Pump in Normal and Noisy Environment with Artificial Neural Network and Support Vector Machine Enhanced by a Genetic Algorithm

Fuel Efficient Truck Platooning with Time Restrictions and Multiple Speeds Solved by a Particle Swarm Optimisation