Acoustic Condition Monitoring & Fault Diagnostics for Industrial Systems

Condition monitoring and fault diagnostics for industrial systems is required for cost reduction, maintenance scheduling, and reducing system failures. Catastrophic failure usually causes significant damage and may cause injury or fatality, making early and accurate fault diagnostics of paramount importance. Existing diagnostics can be improved by augmenting or replacing with acoustic measurements, which have proven advantages over more traditional vibration measurements including, earlier detection of emerging faults, increased diagnostic accuracy, remote sensors and easier setup and operation. However, industry adoption of acoustics remains in relative infancy due to vested confidence and reliance on existing measurement and, perceived difficulties with noise contamination and diagnostic accuracy. Researched acoustic monitoring examples typically employ specialist surface-mount transducers, signal amplification, and complex feature extraction and machine learning algorithms, focusing on noise rejection and fault classification. Usually, techniques are fine-tuned to maximise diagnostic performance for the given problem. The majority investigate mechanical fault modes, particularly Roller Element Bearings (REBs), owing to the mechanical impacts producing detectable acoustic waves. The first contribution of this project is a suitability study into the use of low-cost consumer-grade acoustic sensors for fault diagnostics of six different REB health conditions, comparing against vibration measurements. Experimental results demonstrate superior acoustic performance throughout but particularly at lower rotational speed and axial load. Additionally, inaccuracies caused by dynamic operational parameters (speed in this case), are minimised by novel multi-Support Vector Machine training. The project then expands on existing work to encompass diagnostics for a previously unreported electrical fault mode present on a Brush-Less Direct Current motor drive system. Commonly studied electrical faults, such as a broken rotor bar or squirrel cage, result from mechanical component damage artificially seeded and not spontaneous. Here, electrical fault modes are differentiated as faults caused by issues with the power supply, control system or software (not requiring mechanical damage or triggering intervention). An example studied here is a transient current instability, generated by non-linear interaction of the motor electrical parameters, parasitic components and digital controller realisation. Experimental trials successfully demonstrate real-time feature extraction and further validate consumer-grade sensors for industrial system diagnostics. Moreover, this marks the first known diagnosis of an electrically-seeded fault mode as defined in this work. Finally, approaching an industry-ready diagnostic system, the newly released PYNQ-Z2 Field Programmable Gate Array is used to implement the first known instance of multiple feature extraction algorithms that operate concurrently in continuous real-time. A proposed deep-learning algorithm can analyse the features to determine the optimum feature extraction combination for ongoing continuous monitoring. The proposed black-box, all-in-one solution, is capable of accurate unsupervised diagnostics on almost any application, maintaining excellent diagnostic performance. This marks a major leap forward from fine-tuned feature extraction performed offline for artificially seeded mechanical defects to multiple real-time feature extraction demonstrated on a spontaneous electrical fault mode with a versatile and adaptable system that is low-cost, readily available, with simple setup and operation. The presented concept represents an industry-ready all-in-one acoustic diagnostic solution, that is hoped to increase adoption of acoustic methods, greatly improving diagnostics and minimising catastrophic failures

Towards acoustic condition monitoring for detection and characterisation of laser induced breakdown in a gas turbine laser ignition system

Acoustic detection and characterisation of laser induced breakdown is an attractive proposition in laser ignition systems in which condition monitoring is necessary but where optical access for monitoring purposes is impractical. Presented is a signal processing method based on wavelet decomposition for the non-invasive detection of acoustic emissions resulting from laser induced breakdown in an atmospheric pressure combustion test rig, representative of a single combustion chamber in a sub 15 MW industrial gas turbine. The probability and consistency of laser induced breakdown is determined from the acoustic signal and used to characterize the operating conditions and identify abrupt and incipient or slowly developing faults

Roller element bearing acoustic fault detection using smartphone and consumer microphones

Roller element bearings are a common component and crucial to most rotating machinery; their failure makes up around half of the total machine failures, each with the potential to cause extreme damage, injury and downtime. Fault detection through condition monitoring is of significant importance. This paper demonstrates bearing fault detection using widely accessible consumer audio tools. Audio measurements from a smartphone and a standard USB microphone, and vibration measurements from an accelerometer are collected during tests on an electrical induction machine exhibiting a variety of mechanical bearing anomalies. A peak finding method along with use of trained Support Vector Machines (SVMs) classify the faults. It is shown that the classification rate from both the smartphone and the USB microphone was 95 and 100%, respectively, with the direct physically detected vibration results achieving only 75% classification accuracy. This work opens up the opportunity of using readily affordable and accessible acoustic diagnosis and prognosis for early mechanical anomalies on rotating machines

Continuous Acoustic Monitoring of Electrical Machines; Processing Signals from USB Microphone & Mobile Smartphone Sensors Detecting DC Motor Controller Fault

Transient current instability is one of the most common faults evident in Pulse Width Modulation (PWM) controlled brushless DC motors. This paper explores the under-developed field of real-time acoustic diagnostics for electrically based faults using consumer grade sensors. Current instabilities produce an audible torque transient on the motor, easily detectable using consumer acoustic sensors; a USB microphone and smartphone in this case. Two time-frequency signal processing techniques, Wavelet Packet Transform (WPT) and Empirical Mode Decomposition (EMD), are used to isolate information pertaining to the fault and are assessed for computational performance. This gives four processed signals to search for instabilities using a peak finding technique. We then compare the performance of each method. With the USB microphone WPT signal correlating the best results (93%), a simplistic logarithmic predictive model is used to estimate the durations for the next experimental run, in real-time. The results prove that readily accessible and affordable consumer acoustic sensors can be used for real-time fault diagnostics with a high degree of accuracy

Acoustic Diagnostics of Electrical Origin Fault Modes with Readily Available Consumer-Grade Sensors

Acoustic diagnostics, traditionally associated with mechanical fault modes, can potentially solve a wider range of monitoring applications. Typically, fault modes are induced purposefully by the researcher through physical component damage whilst the system is shutdown. This paper presents low-cost real-time fault diagnostics of a previously unreported acute electrical origin fault that manifests sporadically during system operation with no triggering intervention. A suitability study into acoustic measurements from readily available consumer-grade sensors for low-cost real-time diagnostics of audible faults, and a brief overview of the theory and configuration of the wavelet packet transform (including optimal wavelet selection methods) and empirical mode decomposition processing algorithms is also included. The example electrical origin fault studied here is an unpredictable current instability arising with the PWM-controller of a BrushLess DC motor. Experimental trials positively detect 99.9 % of the 1160 resultant high-bandwidth torque transients using acoustic measurements from a USB microphone and a smartphone. While the use of acoustic techniques for detecting emerging electrical origin faults remains largely unexplored, the techniques demonstrated here can be readily adopted for the prevention of catastrophic failure of drive and power electronic components