Patent Abstract:
Faults are monitored with information from agents for a plurality of sensors located on a plurality of circuit boards. A policy containing a error event thresholds against which the stored sensor information can be compared. Actions can be initiated by a fault module when one or more of the error event thresholds is exceeded.

Full Description:
BACKGROUND 
     As is known in the art, fans can be used to force air flow over circuit devices to dissipate heat. This forced air cooling is a well known thermal management mechanism used for various types of electronic equipment having circuits and circuit boards. A chassis, for example, can contain slots for an array of circuit boards, e.g., blades. A fan tray having a series of fan modules can force air into the chassis to cool the blades. 
     Failure of one or more of the fan modules is undesirable since air flow will be reduced. With reduced air flow into the chassis, the temperature of the components and integrated circuits on the blade may rapidly exceed specified acceptable operating temperatures and stress the components. These conditions can decrease reliability of the equipment and increase the Mean Time Between Failures (MTBF). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The exemplary embodiments contained herein will be more fully understood from the following detailed description taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  is a schematic representation of a fan module having sensors to measure operating characteristics; 
         FIG. 1A  is a pictorial representation of a fan module that can be placed in a fan tray; 
         FIG. 2A  is a perspective view of a chassis having a fan module with sensors; 
         FIG. 2B  is a front view of a chassis having a fan module with sensors; 
         FIG. 2C  is a pictorial representation of air flow through a chassis; 
         FIG. 3  is a schematic depiction of a system having a failure module. 
         FIG. 4  is a flow diagram showing collection of sensor data; 
         FIG. 5  is a flow diagram showing a leaky bucket fault prediction implementation; and 
         FIG. 6  is a block diagram of a system having a fault module. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  shows an exemplary fan module  100  having sensors to monitor various operating characteristics of a cooling fan  102  to detect and/or predict fan failures. As shown in  FIG. 1A , the fan module  100  can be removably placed in a fan tray  101 , which can be coupled to an equipment chassis. The fan sensors can include an input temperature sensor  104   a  for sensing the temperature of air flowing into the fan and an output temperature sensor  104   b  for sensing the temperature of air flowing out of the fan. A current sensor  106  can monitor the current used by the fan module  100  and a voltage sensor  107  can measure a voltage supplied to the fan module. A fan blade speed monitor  108 , such as a tachometer, can monitor the speed of the fan as it rotates. A fan module temperature sensor  110  can monitor the temperature of circuitry and/or fan motor in the fan module that controls the fan. Input and output pressure sensors  112   a,b  can provide a pressure differential between an input and output side of the fan. A noise sensor  114 , which can be provided as a microphone, measures ambient noise proximate the fan module  100 . A vibration sensor  116  monitors vibration levels that may indicate impending mechanical failure or anomaly. 
     The fan sensors collect various information to monitor the health of the cooling subsystem that may be used to predict failures. For example, fan current level and fan rotation speed baseline information can be obtained after initial operation. If the current and/or fan speed deviate from the baseline levels by greater than a predetermined amount, then an alert can be generated. For example, thresholds that are within 30% deviation can be considered non critical. 
     The noise sensor  114  can monitor bearing noise from the fan motor. In one embodiment, the noise information can have one or more thresholds. If the noise rises above a first threshold, a first alert for a first level can be generated, which may indicate fan maintenance is suggested. Noise above a second threshold can generate a second alert for a second level indicating that fan failure is imminent. The noise or sound sensor  114  can be placed internal or external to the fan. The signals to be captured represent both sound power or pressure and sound quality in a frequency bandwidth that may be utilized to indicate the fan or system failure modes by comparing the sound signature to existing sound signatures. 
     The paired pressure sensors  112   a,b  measure a differential between fan intake and outtake air pressure that can form the basis of alerts when the pressure is above or below one or more threshold settings. In an alternative embodiment, pressure differential information is collected by measuring the torque to the fan blades or hub by torque sensor(s) in the blade or hub. In an exemplary embodiment, in a chassis a normal pressure differential is about 0.15″ of H 2 O. An abnormal condition due to clogged filter will increase pressure differential depending on the condition of the filter. 
     In one embodiment, a pressure/torque alert correlates to an abnormally reduced Free Area Ratio (FAR) resulting from, a clogged air filter, cabling blockage, insertion of an extraordinarily densely populated blade, and/or some type of obstacle placed at airflow inlet/outlet, etc. The clog or blockage can cause reduced air flow and as a result higher temperatures inside the chassis. 
     Power can be monitored using information from the current and voltage monitors  106 ,  107 . In one embodiment, a product of electrical current I drawn by the fan and a voltage V supplied to the fan is monitored. The tachometer  108  can also provide rotation speed information. 
     The work performed by the fan in blowing the air corresponds to power (Watts) consumed per rotation in revolutions per minute (RPM) of the fan blade. Assume the work W=V*I/RPM. The computed work over time can be used to evaluate performance of the fan. 
     A gradual increase of an absolute value of W over a period of time suggests:
         1) Possible Filter Clogging—if the work level goes above a predetermined threshold, it is likely that the filter should be replaced.   2) Possible Fan Mechanical failure—a gradual increase in work combined with an increase in fan motor temperature may indicate a possible fan mechanical problem   3) Possible Mechanical Failure—an increase in work in combination with certain noise and/or vibration reading may indicate an impending fan mechanical failure       

     A sudden increase in the amount of work performed by the fan indicates an abrupt change in the airflow condition. Possible causes of the sudden increase include:
         1) Sudden obstruction to air flow in the chassis—For Example: This can be caused by installation of non compliant hardware.   2) Impending Fan Bearing Failure—, an increase in work and certain temperature, noise and vibration may point to imminent fan bearing failure.   3) Mechanical obstruction to Fan Blade rotation—This can be caused by a partial lodging of a foreign obstacle obstructing the free rotation of the fan. Noise and vibration sensor information may help identify this type of failure.       

     Sensor information can be combined to determine the type of failure. For example, if the fan bearing suddenly fails due to high temperature, drying up of lubricants, etc., then there would be a sudden increase in current drawn by the fan motor, a sudden drop in the speed of fan, and possibly, an increase in the noise generated by the fan and also increase in temperature of the fan motor. All these readings can be correlated to predict impending fan failure. 
     The tachometer  108  can be used to measure the fan spin up time, i.e., the time required for the fan to reach its operating speed. This parameter can be logged over many power-on cycles. The increase in spin up time can be used to detect fan degradation and predict fan failure. Blades in a fan are a moving part where a blade assembly rotates around a central axis of the fan. There is typically a bearing mechanism with appropriate lubricant to allow for free rotation of the blade assembly. This bearing mechanism may degrade over time and the lubricant may loose its viscosity. The fan motor may need more power and time to come up to speed. 
     Sensor information can be used together with diagnostic control to measure the performance of the cooling subsystem. For example, during maintenance periods and other low load periods, a fan diagnostics module can be activated and perform some online diagnostics on each fan, which can be performed sequentially on each fan. By running fan diagnostics, the over all cooling performance of the chassis is not impacted if the diagnostics take relatively little time, e.g., seconds. The diagnostics can cycle power to the fans and measure speed up time to record any changes. The recorded speedup time provides an indication of the state of lubrication and friction inside the fan mechanical components. 
     After receiving one or more alerts, a fan controller may increase the fan speed to maintain air flow levels. However, this may reduce the operational life of a fan module or fan tray. While the fan controller increases fan speed to compensate the air cooling loss, the fan controller can also deliver warning message or trigger alarms from its diagnostic system, correlated to the signature of each failure mode, as described below. Such a situation can be recorded as a degraded operating condition. A failure module can monitor the rate and duration of degraded operating conditions and predict failures in cooling subsystem, as described more fully below. 
     An exemplary list of characteristics that can be monitored includes:
         Fan Spin UpTime   Start/Stop Count   Power On Hours Count   Power Cycles   Power consumed   Temperature   Spin High Current   Spin Buzz/Noise   Abnormally high Spin Speed   High pressure differential caused by changed Airflow       

       FIGS. 2A and 2B  show an exemplary chassis  200  having a fan tray  202  containing a series of fan modules  204  to force air into an interior of the chassis, which has slots  206  into which blades can be inserted. As shown in  FIG. 2C  the fan modules  204  can draw air into an input air plenum  208  into the chassis interior and out of the chassis via an output air plenum  210 . 
       FIG. 3  shows an exemplary system  300  having a failure module  302  that can monitor and/or predict failures for a processing blade  304  and a storage blade  306 , for example, based upon operating characteristics from sensors in blades and/or in a cooling system  308 , as described above. The processing blade  304  can include a processor  310 , memory  312 , and an intelligent platform management controller (IPMC)  314 . IPMC is a generic controller that performs various functions including monitoring various operating parameters, such as voltage and temperature on the various components on the platform. 
     The storage blade  306  can include a processor  315 , disks  316   a,b  and an IPMC  318 . The blades can include a variety of integrated circuits, such as processors, programmable logic devices, etc., and discrete components, such as resistors, capacitors, transistors and diodes 
     The failure module  302  can include a series of agents to monitor error information that can be used to predict failures. The agents provide information to the failure module  302  for predicting failures based upon the cooling system  308  and other operating characteristics. In the illustrated embodiment, a fan failure prediction agent  320  is coupled to an IPMC  322  in the cooling system  308 . A first silicon failure prediction agent  324  is coupled to the IPMC  314  in the processor blade  304  and a second silicon failure prediction agent  326  is coupled to the IPMC  318  in the storage blade  306 . A memory failure prediction agent  328  is coupled to the processor  310 /memory  312  and a disk failure prediction agent  330  is coupled to the processor  315  in the storage blade  306 . 
     Integrated circuits on the blades  304 ,  306  are designed to operate under specified temperature, voltage and frequency conditions. Typically these devices are validated to operate in all corners of the operating range. The operating corners could be, for example, operation at or about the upper limit of the allowed environment temperature or other parameter. Validation plans may include stressing devices beyond the normal operating ranges in various combinations of low, normal and high settings. For example, a device will be tested to operate to its full performance in a low voltage limit, a high temperature limit, and a high frequency limit of the rated specification. Based on the level of integration, function, performance, power dissipation, local heat sinks, and local fan devices of devices on the blades, hotspots on the blade may exist. 
     On a typical high performance blade, sensors for temperature are built into various components, such as a processor, memory module, and various chipsets. The temperature sensors are typically in the form of temperature sensing diodes connected to analog-to-digital converters providing temperature data for the silicon in these devices. Temperature sensors generally exist inside the disk drives. The IPMC controller on the blade monitors these temperature sensors and reports the data at a predetermined interval to the requesting software. In one embodiment, sensor data records are part of the IPMC internal data structures. Whenever the measured temperature crosses set thresholds, error alerts are generated by the IPMC. Voltage sensors are implemented in a similar fashion and these monitor the voltage levels on the various power supply rails on the platform. 
     The voltage and temperature probes should be placed as close as possible to the source of power dissipation. It may be noted that the hardware architecture of the blade in the platform can be taken into consideration and the various tolerances that can creep in due to the tolerances of functionality of each individual component. The design may be robust in specifications of performance under various extremes of voltage, frequency and temperature. 
     Even though a device/component/system operates with full performance, for example 100% CPU (central processing unit) load, there may be an increased likelihood that if the device/component/system continues to operate under full performance during corner conditions due to presence of other devices in the system there will be additional dynamic swings of these parameters of voltage, temperature and even frequency. These dynamic swings are likely to cause the device to operate beyond the ratings and can eventually lead to failures. 
     Consider a CPU rated to operate at 2 GHz with a maximum die temperature of 100 Deg C and core operating voltage of 1.9V. Due to a sudden increase in the load on the CPU, the temperature goes beyond 100 deg C, say 105 deg C, for about 30 seconds. This operation for 30 seconds at 105 deg C. is operation beyond the rated operating range. These events of dynamic swings beyond the operating range in one or more parameters at the same time will be captured as critical events of operation. The rate at which these events happen will be an indicator how over stressed the devices are, and will be used to predict the degradation of the system and can lead to eventual failures. As used herein, the term “event” refers to an operating condition when one or more of the operating parameters is beyond the rated setting. 
     The prediction agents  320 ,  324 ,  328 ,  326 ,  330  extract information from the sensors and based on a policy, which specifies the thresholds of lower and upper margins. The fault module  302  can monitor the sensor information to predict the likelihood of failures. 
     In one embodiment, the fault module  302  captures event information and builds a database  350  on each event for each event type and its occurrence frequency. The database is compact and built to contain the following information:
         Sensor ID   Sensor Type   Sensor Policy   Time Stamp   Time Counter       

       FIG. 4  shows an exemplary process to collect sensor data. In processing block  400 , the database to store sensor information is initialized. In block  402 , threads in the prediction agents are initialized. As is well known in the art, a thread is a process that is part of a larger process or program. The prediction agent threads are then monitored, such as in round robin fashion, to collected sensor data in block  404 . In an exemplary embodiment, at a regular frequency the fault module monitors the database for new events and computes a rate of the events to determine if a stress condition has been caused due to the extreme limits of temperature, voltage and frequency and combinations thereof. Stress conditions are recorded the same or different database, which can store policy stress threshold, actual stress count/rate, timestamp, etc. 
     In processing decision block  406 , it is determined whether an agent reported an error condition, such as exceeding a threshold for a given parameter. If not, the prediction agent threads are monitored in block  404 . If so, in processing block  408  alert information is stored in the database for the corresponding device/platform/system, along with other information, such as timestamp. 
     In processing block  410 , the heuristics are compared against predetermined values to determine if the alert rate for the given parameter is greater than a predetermined value in accordance with the alert policy. The term “heuristics” as used herein refers to an application of a predetermined mechanism to determine if the rate of change is above or below the set threshold. In an exemplary embodiment, there is a policy setting for each sensor type. This could be default hard coded for certain sensor types and programmable for other. 
     In processing decision block  412 , it is determined whether the alert rate is above the threshold. If not, in processing block  414  the database is updated with sensor error information and threads are again monitored in block  404 . If the rate was above the threshold, in processing block  416 , an action is initiated based upon the policy set by a user. An action can include, for example, an operator alarm is triggered to notify that the system is overstressed and needs replacement. 
     In one embodiment, the fault module  302  utilizes a so-called leaky bucket counter for each of the above sensors, as shown in  FIG. 5 . In processing block  500 , the database is initialized and in block  502  the agent threads are initialized. In processing block  504 , the leaky bucket counters are initialized for the events for which a count is maintained and monitored. In block  506 , the system waits for a stress event interrupt and the counters are decremented at predetermined time intervals. In decision block  508 , it is determined whether the event count is less than the threshold set in the policy. If so, in block  510 , an action is initiated based on the policy. If not, then in block  512  a timer is. started to count down a predetermined time interval. In decision block  514  it is determined whether the time is expired by examining the value in the timer. If the timer has not expired the timer value is re-examined in block  514 . When the timer has expired, in processing block  516  the leaky bucket counter (LBC) for a given event is incremented. In decision block  518 , it is determined whether the LBC value is greater than a predetermined value set in the policy. If so, the LBC is set to its initial value set by the policy in block  520 . If not, processing continues in block  506 . In summary, a LBC for an event decrements each time a stress event is set and at a periodic rate it is incremented. When the LBC underflows a prediction failure alarm is set. The LBC is reset to its upper limit if no stress events occur. 
       FIG. 6  shows an exemplary fan controller embedded system  260  having a processor  262  running instruction from a failure prediction embedded code module  264 , both of which exchange data with an error database  266  containing error and/or collected sensor information. An intelligent platform management controller (IPMC) communicates with random access memory (RAM)  270  and firmware  272  to exemplary sensors include a fan speed sensor  273 , fan motor temperature sensor  274 , fan voltage sensor  276 , fan vibration sensor  278 , and noise/sound sensor  280 . It is understood that a wide range of further sensor and sensor-types, such as temperature and voltage sensors described above, can be included. 
     Other embodiments are within the scope of the following claims.

Technology Classification (CPC): 6