Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

Conformity assessment refers to activities undertaken to check whether some product, service or process meets certain criteria and specifications given by internationally accepted standards. The decision on whether a property of interest is aligned with the set standards is made based on measurement. However, uncertainty associated with the measurement results may lead to incorrect decisions. Measurement results may be falsely rejected as non-conforming, although they meet specifications. This is referred to as the producer’s risk. If the measurement result that does not meet the required specifications is accepted as conforming, this is referred to as the consumer’s risk. This paper covers calculations of global consumer's and producer's risk using the Bayesian approach and deals with the application of metrics related to confusion matrices in conformity assessments. These techniques have been used to assess the conformity of the bearing ring diameter with the given specifications. Based on the behavior of these metrics, the optimal length of the guard band was determined with the aim of minimizing the global consumer’s and producer’s risk.

One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science.

The reliable operation of a process plant is critical to the safety, performance, and profitability of a business. Failure Mode and Effects Analysis (FMEA) is a process of reviewing systems, subsystems, and equipment that identify potential failure modes, their root causes, and consequences. FMEA is also a risk assessment tool that has been used successfully in a wide range of process industries as an integral part of reliability-centered maintenance, safety management, and continuous improvement. The method has indeed been criticized, especially in the area of system assessment, but engineers still predominantly use traditional, unmodified FMEA best practices. In this study, a new conceptual model is proposed to improve the traditional technique and make FMEA a more autonomous, data-driven, and accurate method. The conceptual model of improved FMEA uses ANFIS and FIS models in one automated process that aims to solve the defect handling process from failure detection to quantification of risk level and prioritization of dedicated mitigation action.

Modern maintenance strategies, such as predictive and prescriptive maintenance, which derived from the concept of Industry and Maintenance 4.0, involve the application of the Industrial Internet of Things (IIoT) to connect maintenance objects enabling data collection and analysis that can help make better decisions on maintenance activities. Data collection is the initial step and the foundation of any modern Predictive or Prescriptive maintenance strategy because it collects data that can then be analysed to provide useful information about the state of maintenance objects. Condition monitoring of rotary equipment is one of the most popular maintenance methods because it can distinguish machine state between multiple fault types. The topic of this paper is the presentation of an automated system for data collection, processing and interpretation of rotary equipment state that is based on IIoT framework consisting of an IIoT accelerometer, edge and fog devices, web API and database. Additionally, ISO 10816-1 guidance has been followed to develop module for evaluation of vibration severity. The collected data is also visualized in a dashboard in a near-real time and shown to maintenance engineering, which is crucial for pattern monitoring. The developed system was launched in laboratory conditions using rotating equipment failure simulator to test the logic of data collection and processing. A proposed system has shown that it is capable of automated periodic data collection and processing from remote places which is achieved using Node RED programming environment and MQTT communication protocol that enables reliable, lightweight, and secure data transmission.