A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect

Abstract

With the rapid progress of network technologies and sensors, monitoring the sensor data such as pressure, temperature, current, vibration and other electrical, mechanical and chemical variables has become much more significant. With the arrival of Big Data and artificial intelligence (AI), sophisticated solutions can be developed to prevent failures and predict the equipment’s remaining useful life (RUL). These techniques allow for taking maintenance actions with haste and precision. Accordingly, this study provides a systematic literature review (SLR) of the predictive maintenance (PdM) techniques in transportation systems. The main focus of this study is the literature covering PdM in the motor vehicles’ industry in the last 5 years. A total of 52 studies were included in the SLR and examined in detail within the scope of our research questions. We provided a summary on statistical, stochastic and AI approaches for PdM applications and their goals, methods, findings, challenges and opportunities. In addition, this study encourages future research by indicating the areas that have not yet been studied in the PdM literature.

Global Models of Smart Cities and Potential IoT Applications: A Review

“…IoT sensors can be used to monitor the condition of vehicles and equipment in real time and predict when maintenance is needed. This can help companies reduce downtime and extend the life of their assets [44,45]. Overall, logistic transportation IoT services can provide companies with a range of benefits, including improved efficiency, reduced costs, increased safety, and enhanced customer satisfaction.…”

Implementation of a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory Autoencoder for Anomaly Detection on Multivariate Timeseries Data of Industrial Blower Ball Bearing Units

“…We recall that the current shape of the training set is (4185, 2), where 4185 is the number of observations and 2 is the number of variables (V-RMS and Temperature), while the input to an LSTM layer is in the form of (batch_size, time_steps, features). A satisfactory course of action is achieved by producing overlapping windows of 24 consecutive observations, resulting in a final three-dimensional training set of shape (4162, 24,2), consisting of 4162 sequences of 24 successive records; each sequence element is represented by its two attribute values. The same sequential transformation applies to every dataset presented in this work.…”

“…Additionally, a PdM systematic literature review recently carried out by O.Ö. Ersöz et al is available in [ 24 ], aiming specifically for the field of transportation systems.…”

Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment

“…This proactive approach not only extends the lifespan of equipment but also optimizes operational continuity and enhances overall productivity [8]. The significance of predictive maintenance extends across a multitude of sectors, ranging from manufacturing and energy production to transportation and healthcare [9][10][11]. As organizations seek ways to minimize operational disruptions, reduce maintenance costs, and maximize the value of their assets, the adoption of predictive maintenance strategies has become a pivotal step toward achieving these goals [12].…”