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In this work, locally sourced non-hazardous materials were used to produce brake pad using grey relational analysis (GRA) and experimental design via central composite design. Raw materials selected for production include coconut shell, epoxy resin (binder), graphite (friction modifier) and aluminum oxide (abrasive). Twenty-seven samples were produced separately using coconut shell as reinforcement material by varying process parameters. Formulation of the brake pads samples was done using rule of mixture and a weight percent of 52% reinforcement material, 35% binder, 8% abrasive and 5% friction modifier were used for the production. Grey relational analysis (GRA) shows that optimal process performance can be obtained using molding pressure, molding temperature, curing time and heat treatment time of 14 MPa, 140 °C, 8 min and 5 h, respectively. Optimized sample was produced using the optimal set of process parameters obtained from GRA and compared with commercially available sample produced by Ibeto Group. The experimental results showed that the performance of the optimized coconut shell-reinforced brake pad compared satisfactorily with commercially available samples and capable of producing less brake noise and vibration during application. Analysis of variance shows that curing time with a contribution of 30.38% and 31.40% have the most significant effect on the hardness and ultimate tensile strength of the coconut shell-reinforced friction material, respectively, while heat treatment time with a contribution of 46.3% and 24.23% have the most significant effect on the wear rate and friction coefficient of coconut shell-reinforced brake pad, respectively. The effects of all the factors on the properties of the friction materials are significant since their p values are greater than 0.010 (1%). Keywords Brake pads • Central composite design • Grey relational analysis • Coconut shell and characterization List of symbols Axial value µ Coefficient of friction Distinguishing coefficient u Percentage weight δ Densities f Volume fraction X Weight F Applied load d Disk diameter t Time of exposure of specimen to abrasion N Radial speed b Support span (mm) t Specimen thickness (mm) Deflection (mm) w Specimen width (mm) χ Distinguishing coefficient n Number of factor level combination k Performance value MT Molding temperature MP Molding pressure CT Curing time HTT Heat treatment time

In this study, we present the use of an internet of things (IoT) analytics platform service to mimic real-time pipeline monitoring and determine the location of damage on a pipeline. Pressure pulses, based on the principle of vibration in pipes are used for pipeline monitoring in this study. The principle of time delay between pulse arrivals at sensor positions is also adopted in this study. An Arduino and a Wi-Fi module were combined, programmed and used to produce a wireless communication device which communicates with the ThingSpeak internet of things (IoT) analytics platform. A total of five channels were created on the platform to collect data from the five sensors that were used in the experimental test rig that made use of wireless communication device. Signal data was collected once every 15 s and all the channels were updated every 2 min. ThingSpeak provided instant visualizations of data posted by the wireless communication device. Online analysis and processing of the data was performed as it came in. A second test rig was built that made use of a data logger for processing of data. The measured velocity of pulse propagation using the data logger and air as transport fluid was 355 m/s. The computed estimates of event location for the 50 measurements taken ranged between 4.243 m and 4.246 m. This had a scatter of just 3 mm against the actual measured event location of 4.23 m. The experimental results obtained showed that the performance of the wireless communication device compared satisfactorily with the data logger and is capable of detecting the location of damage on real pipelines when used for real time monitoring.Using this communication device and an analytics platform, real-time monitoring of pipelines can be carried out from any location in the world on any internet-enabled device.